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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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Research Hypothesis In Psychology: Types, & Examples

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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On This Page:

A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

Educational resources and simple solutions for your research journey

What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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Hypothesis n., plural: hypotheses [/haɪˈpɑːθəsɪs/] Definition: Testable scientific prediction

Table of Contents

What Is Hypothesis?

A scientific hypothesis is a foundational element of the scientific method . It’s a testable statement proposing a potential explanation for natural phenomena. The term hypothesis means “little theory” . A hypothesis is a short statement that can be tested and gives a possible reason for a phenomenon or a possible link between two variables . In the setting of scientific research, a hypothesis is a tentative explanation or statement that can be proven wrong and is used to guide experiments and empirical research.

It is an important part of the scientific method because it gives a basis for planning tests, gathering data, and judging evidence to see if it is true and could help us understand how natural things work. Several hypotheses can be tested in the real world, and the results of careful and systematic observation and analysis can be used to support, reject, or improve them.

Researchers and scientists often use the word hypothesis to refer to this educated guess . These hypotheses are firmly established based on scientific principles and the rigorous testing of new technology and experiments .

For example, in astrophysics, the Big Bang Theory is a working hypothesis that explains the origins of the universe and considers it as a natural phenomenon. It is among the most prominent scientific hypotheses in the field.

“The scientific method: steps, terms, and examples” by Scishow:

Biology definition: A hypothesis  is a supposition or tentative explanation for (a group of) phenomena, (a set of) facts, or a scientific inquiry that may be tested, verified or answered by further investigation or methodological experiment. It is like a scientific guess . It’s an idea or prediction that scientists make before they do experiments. They use it to guess what might happen and then test it to see if they were right. It’s like a smart guess that helps them learn new things. A scientific hypothesis that has been verified through scientific experiment and research may well be considered a scientific theory .

Etymology: The word “hypothesis” comes from the Greek word “hupothesis,” which means “a basis” or “a supposition.” It combines “hupo” (under) and “thesis” (placing). Synonym:   proposition; assumption; conjecture; postulate Compare:   theory See also: null hypothesis

Characteristics Of Hypothesis

A useful hypothesis must have the following qualities:

• It should never be written as a question.
• You should be able to test it in the real world to see if it’s right or wrong.
• It needs to be clear and exact.
• It should list the factors that will be used to figure out the relationship.
• It should only talk about one thing. You can make a theory in either a descriptive or form of relationship.
• It shouldn’t go against any natural rule that everyone knows is true. Verification will be done well with the tools and methods that are available.
• It should be written in as simple a way as possible so that everyone can understand it.
• It must explain what happened to make an answer necessary.
• It should be testable in a fair amount of time.
• It shouldn’t say different things.

Sources Of Hypothesis

Sources of hypothesis are:

• Patterns of similarity between the phenomenon under investigation and existing hypotheses.
• Insights derived from prior research, concurrent observations, and insights from opposing perspectives.
• The formulations are derived from accepted scientific theories and proposed by researchers.
• In research, it’s essential to consider hypothesis as different subject areas may require various hypotheses (plural form of hypothesis). Researchers also establish a significance level to determine the strength of evidence supporting a hypothesis.
• Individual cognitive processes also contribute to the formation of hypotheses.

One hypothesis is a tentative explanation for an observation or phenomenon. It is based on prior knowledge and understanding of the world, and it can be tested by gathering and analyzing data. Observed facts are the data that are collected to test a hypothesis. They can support or refute the hypothesis.

For example, the hypothesis that “eating more fruits and vegetables will improve your health” can be tested by gathering data on the health of people who eat different amounts of fruits and vegetables. If the people who eat more fruits and vegetables are healthier than those who eat less fruits and vegetables, then the hypothesis is supported.

Hypotheses are essential for scientific inquiry. They help scientists to focus their research, to design experiments, and to interpret their results. They are also essential for the development of scientific theories.

Types Of Hypothesis

In research, you typically encounter two types of hypothesis: the alternative hypothesis (which proposes a relationship between variables) and the null hypothesis (which suggests no relationship).

Simple Hypothesis

It illustrates the association between one dependent variable and one independent variable. For instance, if you consume more vegetables, you will lose weight more quickly. Here, increasing vegetable consumption is the independent variable, while weight loss is the dependent variable.

Complex Hypothesis

It exhibits the relationship between at least two dependent variables and at least two independent variables. Eating more vegetables and fruits results in weight loss, radiant skin, and a decreased risk of numerous diseases, including heart disease.

Directional Hypothesis

It shows that a researcher wants to reach a certain goal. The way the factors are related can also tell us about their nature. For example, four-year-old children who eat well over a time of five years have a higher IQ than children who don’t eat well. This shows what happened and how it happened.

Non-directional Hypothesis

When there is no theory involved, it is used. It is a statement that there is a connection between two variables, but it doesn’t say what that relationship is or which way it goes.

Null Hypothesis

It says something that goes against the theory. It’s a statement that says something is not true, and there is no link between the independent and dependent factors. “H 0 ” represents the null hypothesis.

Associative and Causal Hypothesis

When a change in one variable causes a change in the other variable, this is called the associative hypothesis . The causal hypothesis, on the other hand, says that there is a cause-and-effect relationship between two or more factors.

Examples Of Hypothesis

Examples of simple hypotheses:

• Students who consume breakfast before taking a math test will have a better overall performance than students who do not consume breakfast.
• Students who experience test anxiety before an English examination will get lower scores than students who do not experience test anxiety.
• Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone, is a statement that suggests that drivers who talk on the phone while driving are more likely to make mistakes.

Examples of a complex hypothesis:

• Individuals who consume a lot of sugar and don’t get much exercise are at an increased risk of developing depression.
• Younger people who are routinely exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces, according to a new study.
• Increased levels of air pollution led to higher rates of respiratory illnesses, which in turn resulted in increased costs for healthcare for the affected communities.

Examples of Directional Hypothesis:

• The crop yield will go up a lot if the amount of fertilizer is increased.
• Patients who have surgery and are exposed to more stress will need more time to get better.
• Increasing the frequency of brand advertising on social media will lead to a significant increase in brand awareness among the target audience.

Examples of Non-Directional Hypothesis (or Two-Tailed Hypothesis):

• The test scores of two groups of students are very different from each other.
• There is a link between gender and being happy at work.
• There is a correlation between the amount of caffeine an individual consumes and the speed with which they react.

Examples of a null hypothesis:

• Children who receive a new reading intervention will have scores that are different than students who do not receive the intervention.
• The results of a memory recall test will not reveal any significant gap in performance between children and adults.
• There is not a significant relationship between the number of hours spent playing video games and academic performance.

Examples of Associative Hypothesis:

• There is a link between how many hours you spend studying and how well you do in school.
• Drinking sugary drinks is bad for your health as a whole.
• There is an association between socioeconomic status and access to quality healthcare services in urban neighborhoods.

Functions Of Hypothesis

The research issue can be understood better with the help of a hypothesis, which is why developing one is crucial. The following are some of the specific roles that a hypothesis plays: (Rashid, Apr 20, 2022)

• A hypothesis gives a study a point of concentration. It enlightens us as to the specific characteristics of a study subject we need to look into.
• It instructs us on what data to acquire as well as what data we should not collect, giving the study a focal point .
• The development of a hypothesis improves objectivity since it enables the establishment of a focal point.
• A hypothesis makes it possible for us to contribute to the development of the theory. Because of this, we are in a position to definitively determine what is true and what is untrue .

How will Hypothesis help in the Scientific Method?

• The scientific method begins with observation and inquiry about the natural world when formulating research questions. Researchers can refine their observations and queries into specific, testable research questions with the aid of hypothesis. They provide an investigation with a focused starting point.
• Hypothesis generate specific predictions regarding the expected outcomes of experiments or observations. These forecasts are founded on the researcher’s current knowledge of the subject. They elucidate what researchers anticipate observing if the hypothesis is true.
• Hypothesis direct the design of experiments and data collection techniques. Researchers can use them to determine which variables to measure or manipulate, which data to obtain, and how to conduct systematic and controlled research.
• Following the formulation of a hypothesis and the design of an experiment, researchers collect data through observation, measurement, or experimentation. The collected data is used to verify the hypothesis’s predictions.
• Hypothesis establish the criteria for evaluating experiment results. The observed data are compared to the predictions generated by the hypothesis. This analysis helps determine whether empirical evidence supports or refutes the hypothesis.
• The results of experiments or observations are used to derive conclusions regarding the hypothesis. If the data support the predictions, then the hypothesis is supported. If this is not the case, the hypothesis may be revised or rejected, leading to the formulation of new queries and hypothesis.
• The scientific approach is iterative, resulting in new hypothesis and research issues from previous trials. This cycle of hypothesis generation, testing, and refining drives scientific progress.

Importance Of Hypothesis

• Hypothesis are testable statements that enable scientists to determine if their predictions are accurate. This assessment is essential to the scientific method, which is based on empirical evidence.
• Hypothesis serve as the foundation for designing experiments or data collection techniques. They can be used by researchers to develop protocols and procedures that will produce meaningful results.
• Hypothesis hold scientists accountable for their assertions. They establish expectations for what the research should reveal and enable others to assess the validity of the findings.
• Hypothesis aid in identifying the most important variables of a study. The variables can then be measured, manipulated, or analyzed to determine their relationships.
• Hypothesis assist researchers in allocating their resources efficiently. They ensure that time, money, and effort are spent investigating specific concerns, as opposed to exploring random concepts.
• Testing hypothesis contribute to the scientific body of knowledge. Whether or not a hypothesis is supported, the results contribute to our understanding of a phenomenon.
• Hypothesis can result in the creation of theories. When supported by substantive evidence, hypothesis can serve as the foundation for larger theoretical frameworks that explain complex phenomena.
• Beyond scientific research, hypothesis play a role in the solution of problems in a variety of domains. They enable professionals to make educated assumptions about the causes of problems and to devise solutions.

Research Hypotheses: Did you know that a hypothesis refers to an educated guess or prediction about the outcome of a research study?

It’s like a roadmap guiding researchers towards their destination of knowledge. Just like a compass points north, a well-crafted hypothesis points the way to valuable discoveries in the world of science and inquiry.

Choose the best answer. 

Send Your Results (Optional)

Further reading.

• RNA-DNA World Hypothesis
• BYJU’S. (2023). Hypothesis. Retrieved 01 Septermber 2023, from https://byjus.com/physics/hypothesis/#sources-of-hypothesis
• Collegedunia. (2023). Hypothesis. Retrieved 1 September 2023, from https://collegedunia.com/exams/hypothesis-science-articleid-7026#d
• Hussain, D. J. (2022). Hypothesis. Retrieved 01 September 2023, from https://mmhapu.ac.in/doc/eContent/Management/JamesHusain/Research%20Hypothesis%20-Meaning,%20Nature%20&%20Importance-Characteristics%20of%20Good%20%20Hypothesis%20Sem2.pdf
• Media, D. (2023). Hypothesis in the Scientific Method. Retrieved 01 September 2023, from https://www.verywellmind.com/what-is-a-hypothesis-2795239#toc-hypotheses-examples
• Rashid, M. H. A. (Apr 20, 2022). Research Methodology. Retrieved 01 September 2023, from https://limbd.org/hypothesis-definitions-functions-characteristics-types-errors-the-process-of-testing-a-hypothesis-hypotheses-in-qualitative-research/#:~:text=Functions%20of%20a%20Hypothesis%3A&text=Specifically%2C%20a%20hypothesis%20serves%20the,providing%20focus%20to%20the%20study.

©BiologyOnline.com. Content provided and moderated by Biology Online Editors.

Last updated on September 8th, 2023

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scientific hypothesis

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• National Center for Biotechnology Information - PubMed Central - On the scope of scientific hypotheses
• LiveScience - What is a scientific hypothesis?
• The Royal Society - Open Science - On the scope of scientific hypotheses

scientific hypothesis , an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an “If…then” statement summarizing the idea and in the ability to be supported or refuted through observation and experimentation. The notion of the scientific hypothesis as both falsifiable and testable was advanced in the mid-20th century by Austrian-born British philosopher Karl Popper .

The formulation and testing of a hypothesis is part of the scientific method , the approach scientists use when attempting to understand and test ideas about natural phenomena. The generation of a hypothesis frequently is described as a creative process and is based on existing scientific knowledge, intuition , or experience. Therefore, although scientific hypotheses commonly are described as educated guesses, they actually are more informed than a guess. In addition, scientists generally strive to develop simple hypotheses, since these are easier to test relative to hypotheses that involve many different variables and potential outcomes. Such complex hypotheses may be developed as scientific models ( see scientific modeling ).

Depending on the results of scientific evaluation, a hypothesis typically is either rejected as false or accepted as true. However, because a hypothesis inherently is falsifiable, even hypotheses supported by scientific evidence and accepted as true are susceptible to rejection later, when new evidence has become available. In some instances, rather than rejecting a hypothesis because it has been falsified by new evidence, scientists simply adapt the existing idea to accommodate the new information. In this sense a hypothesis is never incorrect but only incomplete.

The investigation of scientific hypotheses is an important component in the development of scientific theory . Hence, hypotheses differ fundamentally from theories; whereas the former is a specific tentative explanation and serves as the main tool by which scientists gather data, the latter is a broad general explanation that incorporates data from many different scientific investigations undertaken to explore hypotheses.

Countless hypotheses have been developed and tested throughout the history of science . Several examples include the idea that living organisms develop from nonliving matter, which formed the basis of spontaneous generation , a hypothesis that ultimately was disproved (first in 1668, with the experiments of Italian physician Francesco Redi , and later in 1859, with the experiments of French chemist and microbiologist Louis Pasteur ); the concept proposed in the late 19th century that microorganisms cause certain diseases (now known as germ theory ); and the notion that oceanic crust forms along submarine mountain zones and spreads laterally away from them ( seafloor spreading hypothesis ).

What Is a Hypothesis? (Science)

If...,Then...

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• Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
• B.A., Physics and Mathematics, Hastings College

A hypothesis (plural hypotheses) is a proposed explanation for an observation. The definition depends on the subject.

In science, a hypothesis is part of the scientific method. It is a prediction or explanation that is tested by an experiment. Observations and experiments may disprove a scientific hypothesis, but can never entirely prove one.

In the study of logic, a hypothesis is an if-then proposition, typically written in the form, "If X , then Y ."

In common usage, a hypothesis is simply a proposed explanation or prediction, which may or may not be tested.

Writing a Hypothesis

Most scientific hypotheses are proposed in the if-then format because it's easy to design an experiment to see whether or not a cause and effect relationship exists between the independent variable and the dependent variable . The hypothesis is written as a prediction of the outcome of the experiment.

Null Hypothesis and Alternative Hypothesis

Statistically, it's easier to show there is no relationship between two variables than to support their connection. So, scientists often propose the null hypothesis . The null hypothesis assumes changing the independent variable will have no effect on the dependent variable.

In contrast, the alternative hypothesis suggests changing the independent variable will have an effect on the dependent variable. Designing an experiment to test this hypothesis can be trickier because there are many ways to state an alternative hypothesis.

For example, consider a possible relationship between getting a good night's sleep and getting good grades. The null hypothesis might be stated: "The number of hours of sleep students get is unrelated to their grades" or "There is no correlation between hours of sleep and grades."

An experiment to test this hypothesis might involve collecting data, recording average hours of sleep for each student and grades. If a student who gets eight hours of sleep generally does better than students who get four hours of sleep or 10 hours of sleep, the hypothesis might be rejected.

But the alternative hypothesis is harder to propose and test. The most general statement would be: "The amount of sleep students get affects their grades." The hypothesis might also be stated as "If you get more sleep, your grades will improve" or "Students who get nine hours of sleep have better grades than those who get more or less sleep."

In an experiment, you can collect the same data, but the statistical analysis is less likely to give you a high confidence limit.

Usually, a scientist starts out with the null hypothesis. From there, it may be possible to propose and test an alternative hypothesis, to narrow down the relationship between the variables.

Example of a Hypothesis

Examples of a hypothesis include:

• If you drop a rock and a feather, (then) they will fall at the same rate.
• Plants need sunlight in order to live. (if sunlight, then life)
• Eating sugar gives you energy. (if sugar, then energy)
• White, Jay D.  Research in Public Administration . Conn., 1998.
• Schick, Theodore, and Lewis Vaughn.  How to Think about Weird Things: Critical Thinking for a New Age . McGraw-Hill Higher Education, 2002.
• Scientific Method Flow Chart
• What Are the Elements of a Good Hypothesis?
• Six Steps of the Scientific Method
• What Are Examples of a Hypothesis?
• What Is a Testable Hypothesis?
• Null Hypothesis Examples
• Scientific Hypothesis Examples
• Scientific Variable
• Scientific Method Vocabulary Terms
• Understanding Simple vs Controlled Experiments
• What Is a Controlled Experiment?
• What Is an Experimental Constant?
• What Is the Difference Between a Control Variable and Control Group?
• DRY MIX Experiment Variables Acronym
• Random Error vs. Systematic Error
• The Role of a Controlled Variable in an Experiment

What is a scientific hypothesis?

It's the initial building block in the scientific method.

Hypothesis basics

What makes a hypothesis testable.

• Types of hypotheses
• Hypothesis versus theory

Additional resources

Bibliography.

A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method . Many describe it as an "educated guess" based on prior knowledge and observation. While this is true, a hypothesis is more informed than a guess. While an "educated guess" suggests a random prediction based on a person's expertise, developing a hypothesis requires active observation and background research. 

The basic idea of a hypothesis is that there is no predetermined outcome. For a solution to be termed a scientific hypothesis, it has to be an idea that can be supported or refuted through carefully crafted experimentation or observation. This concept, called falsifiability and testability, was advanced in the mid-20th century by Austrian-British philosopher Karl Popper in his famous book "The Logic of Scientific Discovery" (Routledge, 1959).

A key function of a hypothesis is to derive predictions about the results of future experiments and then perform those experiments to see whether they support the predictions.

A hypothesis is usually written in the form of an if-then statement, which gives a possibility (if) and explains what may happen because of the possibility (then). The statement could also include "may," according to California State University, Bakersfield .

Here are some examples of hypothesis statements:

• If garlic repels fleas, then a dog that is given garlic every day will not get fleas.
• If sugar causes cavities, then people who eat a lot of candy may be more prone to cavities.
• If ultraviolet light can damage the eyes, then maybe this light can cause blindness.

A useful hypothesis should be testable and falsifiable. That means that it should be possible to prove it wrong. A theory that can't be proved wrong is nonscientific, according to Karl Popper's 1963 book " Conjectures and Refutations ."

An example of an untestable statement is, "Dogs are better than cats." That's because the definition of "better" is vague and subjective. However, an untestable statement can be reworded to make it testable. For example, the previous statement could be changed to this: "Owning a dog is associated with higher levels of physical fitness than owning a cat." With this statement, the researcher can take measures of physical fitness from dog and cat owners and compare the two.

Types of scientific hypotheses

In an experiment, researchers generally state their hypotheses in two ways. The null hypothesis predicts that there will be no relationship between the variables tested, or no difference between the experimental groups. The alternative hypothesis predicts the opposite: that there will be a difference between the experimental groups. This is usually the hypothesis scientists are most interested in, according to the University of Miami .

For example, a null hypothesis might state, "There will be no difference in the rate of muscle growth between people who take a protein supplement and people who don't." The alternative hypothesis would state, "There will be a difference in the rate of muscle growth between people who take a protein supplement and people who don't."

If the results of the experiment show a relationship between the variables, then the null hypothesis has been rejected in favor of the alternative hypothesis, according to the book " Research Methods in Psychology " (​​BCcampus, 2015). 

There are other ways to describe an alternative hypothesis. The alternative hypothesis above does not specify a direction of the effect, only that there will be a difference between the two groups. That type of prediction is called a two-tailed hypothesis. If a hypothesis specifies a certain direction — for example, that people who take a protein supplement will gain more muscle than people who don't — it is called a one-tailed hypothesis, according to William M. K. Trochim , a professor of Policy Analysis and Management at Cornell University.

Sometimes, errors take place during an experiment. These errors can happen in one of two ways. A type I error is when the null hypothesis is rejected when it is true. This is also known as a false positive. A type II error occurs when the null hypothesis is not rejected when it is false. This is also known as a false negative, according to the University of California, Berkeley . 

A hypothesis can be rejected or modified, but it can never be proved correct 100% of the time. For example, a scientist can form a hypothesis stating that if a certain type of tomato has a gene for red pigment, that type of tomato will be red. During research, the scientist then finds that each tomato of this type is red. Though the findings confirm the hypothesis, there may be a tomato of that type somewhere in the world that isn't red. Thus, the hypothesis is true, but it may not be true 100% of the time.

Scientific theory vs. scientific hypothesis

The best hypotheses are simple. They deal with a relatively narrow set of phenomena. But theories are broader; they generally combine multiple hypotheses into a general explanation for a wide range of phenomena, according to the University of California, Berkeley . For example, a hypothesis might state, "If animals adapt to suit their environments, then birds that live on islands with lots of seeds to eat will have differently shaped beaks than birds that live on islands with lots of insects to eat." After testing many hypotheses like these, Charles Darwin formulated an overarching theory: the theory of evolution by natural selection.

"Theories are the ways that we make sense of what we observe in the natural world," Tanner said. "Theories are structures of ideas that explain and interpret facts." 

• Read more about writing a hypothesis, from the American Medical Writers Association.
• Find out why a hypothesis isn't always necessary in science, from The American Biology Teacher.
• Learn about null and alternative hypotheses, from Prof. Essa on YouTube .

Encyclopedia Britannica. Scientific Hypothesis. Jan. 13, 2022. https://www.britannica.com/science/scientific-hypothesis

Karl Popper, "The Logic of Scientific Discovery," Routledge, 1959.

California State University, Bakersfield, "Formatting a testable hypothesis." https://www.csub.edu/~ddodenhoff/Bio100/Bio100sp04/formattingahypothesis.htm  

Karl Popper, "Conjectures and Refutations," Routledge, 1963.

Price, P., Jhangiani, R., & Chiang, I., "Research Methods of Psychology — 2nd Canadian Edition," BCcampus, 2015.‌

University of Miami, "The Scientific Method" http://www.bio.miami.edu/dana/161/evolution/161app1_scimethod.pdf  

William M.K. Trochim, "Research Methods Knowledge Base," https://conjointly.com/kb/hypotheses-explained/  

University of California, Berkeley, "Multiple Hypothesis Testing and False Discovery Rate" https://www.stat.berkeley.edu/~hhuang/STAT141/Lecture-FDR.pdf  

University of California, Berkeley, "Science at multiple levels" https://undsci.berkeley.edu/article/0_0_0/howscienceworks_19

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What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

17 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

Hi” best wishes to you and your very nice blog” 

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Theory vs. Hypothesis: Basics of the Scientific Method

Written by MasterClass

Last updated: Jun 7, 2021 • 2 min read

Though you may hear the terms "theory" and "hypothesis" used interchangeably, these two scientific terms have drastically different meanings in the world of science.

Frequently asked questions

What is a hypothesis.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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What is Hypothesis? Definition, Meaning, Characteristics, Sources

• Post last modified: 10 January 2022
• Reading time: 18 mins read
• Post category: Research Methodology

• What is Hypothesis?

Hypothesis is a prediction of the outcome of a study. Hypotheses are drawn from theories and research questions or from direct observations. In fact, a research problem can be formulated as a hypothesis. To test the hypothesis we need to formulate it in terms that can actually be analysed with statistical tools.

As an example, if we want to explore whether using a specific teaching method at school will result in better school marks (research question), the hypothesis could be that the mean school marks of students being taught with that specific teaching method will be higher than of those being taught using other methods.

In this example, we stated a hypothesis about the expected differences between groups. Other hypotheses may refer to correlations between variables.

Table of Content

• 1 What is Hypothesis?
• 2 Hypothesis Definition
• 3 Meaning of Hypothesis
• 4.1 Conceptual Clarity
• 4.2 Need of empirical referents
• 4.3 Hypothesis should be specific
• 4.4 Hypothesis should be within the ambit of the available research techniques
• 4.5 Hypothesis should be consistent with the theory
• 4.6 Hypothesis should be concerned with observable facts and empirical events
• 4.7 Hypothesis should be simple
• 5.1 Observation
• 5.2 Analogies
• 5.4 State of Knowledge
• 5.5 Culture
• 5.6 Continuity of Research
• 6.1 Null Hypothesis
• 6.2 Alternative Hypothesis

Thus, to formulate a hypothesis, we need to refer to the descriptive statistics (such as the mean final marks), and specify a set of conditions about these statistics (such as a difference between the means, or in a different example, a positive or negative correlation). The hypothesis we formulate applies to the population of interest.

The null hypothesis makes a statement that no difference exists (see Pyrczak, 1995, pp. 75-84).

Hypothesis Definition

A hypothesis is ‘a guess or supposition as to the existence of some fact or law which will serve to explain a connection of facts already known to exist.’ – J. E. Creighton & H. R. Smart

Hypothesis is ‘a proposition not known to be definitely true or false, examined for the sake of determining the consequences which would follow from its truth.’ – Max Black

Hypothesis is ‘a proposition which can be put to a test to determine validity and is useful for further research.’ – W. J. Goode and P. K. Hatt

A hypothesis is a proposition, condition or principle which is assumed, perhaps without belief, in order to draw out its logical consequences and by this method to test its accord with facts which are known or may be determined. – Webster’s New International Dictionary of the English Language (1956)

Meaning of Hypothesis

From the above mentioned definitions of hypothesis, its meaning can be explained in the following ways.

• At the primary level, a hypothesis is the possible and probable explanation of the sequence of happenings or data.
• Sometimes, hypothesis may emerge from an imagination, common sense or a sudden event.
• Hypothesis can be a probable answer to the research problem undertaken for study. 4. Hypothesis may not always be true. It can get disproven. In other words, hypothesis need not always be a true proposition.
• Hypothesis, in a sense, is an attempt to present the interrelations that exist in the available data or information.
• Hypothesis is not an individual opinion or community thought. Instead, it is a philosophical means which is to be used for research purpose. Hypothesis is not to be considered as the ultimate objective; rather it is to be taken as the means of explaining scientifically the prevailing situation.

The concept of hypothesis can further be explained with the help of some examples. Lord Keynes, in his theory of national income determination, made a hypothesis about the consumption function. He stated that the consumption expenditure of an individual or an economy as a whole is dependent on the level of income and changes in a certain proportion.

Later, this proposition was proved in the statistical research carried out by Prof. Simon Kuznets. Matthus, while studying the population, formulated a hypothesis that population increases faster than the supply of food grains. Population studies of several countries revealed that this hypothesis is true.

Validation of the Malthus’ hypothesis turned it into a theory and when it was tested in many other countries it became the famous Malthus’ Law of Population. It thus emerges that when a hypothesis is tested and proven, it becomes a theory. The theory, when found true in different times and at different places, becomes the law. Having understood the concept of hypothesis, few hypotheses can be formulated in the areas of commerce and economics.

• Population growth moderates with the rise in per capita income.
• Sales growth is positively linked with the availability of credit.
• Commerce education increases the employability of the graduate students.
• High rates of direct taxes prompt people to evade taxes.
• Good working conditions improve the productivity of employees.
• Advertising is the most effecting way of promoting sales than any other scheme.
• Higher Debt-Equity Ratio increases the probability of insolvency.
• Economic reforms in India have made the public sector banks more efficient and competent.
• Foreign direct investment in India has moved in those sectors which offer higher rate of profit.
• There is no significant association between credit rating and investment of fund.

Characteristics of Hypothesis

Not all the hypotheses are good and useful from the point of view of research. It is only a few hypotheses satisfying certain criteria that are good, useful and directive in the research work undertaken. The characteristics of such a useful hypothesis can be listed as below:

Conceptual Clarity

Need of empirical referents, hypothesis should be specific, hypothesis should be within the ambit of the available research techniques, hypothesis should be consistent with the theory, hypothesis should be concerned with observable facts and empirical events, hypothesis should be simple.

The concepts used while framing hypothesis should be crystal clear and unambiguous. Such concepts must be clearly defined so that they become lucid and acceptable to everyone. How are the newly developed concepts interrelated and how are they linked with the old one is to be very clear so that the hypothesis framed on their basis also carries the same clarity.

A hypothesis embodying unclear and ambiguous concepts can to a great extent undermine the successful completion of the research work.

A hypothesis can be useful in the research work undertaken only when it has links with some empirical referents. Hypothesis based on moral values and ideals are useless as they cannot be tested. Similarly, hypothesis containing opinions as good and bad or expectation with respect to something are not testable and therefore useless.

For example, ‘current account deficit can be lowered if people change their attitude towards gold’ is a hypothesis encompassing expectation. In case of such a hypothesis, the attitude towards gold is something which cannot clearly be described and therefore a hypothesis which embodies such an unclean thing cannot be tested and proved or disproved. In short, the hypothesis should be linked with some testable referents.

For the successful conduction of research, it is necessary that the hypothesis is specific and presented in a precise manner. Hypothesis which is general, too ambitious and grandiose in scope is not to be made as such hypothesis cannot be easily put to test. A hypothesis is to be based on such concepts which are precise and empirical in nature. A hypothesis should give a clear idea about the indicators which are to be used.

For example, a hypothesis that economic power is increasingly getting concentrated in a few hands in India should enable us to define the concept of economic power. It should be explicated in terms of measurable indicator like income, wealth, etc. Such specificity in the formulation of a hypothesis ensures that the research is practicable and significant.

While framing the hypothesis, the researcher should be aware of the available research techniques and should see that the hypothesis framed is testable on the basis of them. In other words, a hypothesis should be researchable and for this it is important that a due thought has been given to the methods and techniques which can be used to measure the concepts and variables embodied in the hypothesis.

It does not however mean that hypotheses which are not testable with the available techniques of research are not to be made. If the problem is too significant and therefore the hypothesis framed becomes too ambitious and complex, it’s testing becomes possible with the development of new research techniques or the hypothesis itself leads to the development of new research techniques.

A hypothesis must be related to the existing theory or should have a theoretical orientation. The growth of knowledge takes place in the sequence of facts, hypothesis, theory and law or principles. It means the hypothesis should have a correspondence with the existing facts and theory.

If the hypothesis is related to some theory, the research work will enable us to support, modify or refute the existing theory. Theoretical orientation of the hypothesis ensures that it becomes scientifically useful. According to Prof. Goode and Prof. Hatt, research work can contribute to the existing knowledge only when the hypothesis is related with some theory.

This enables us to explain the observed facts and situations and also verify the framed hypothesis. In the words of Prof. Cohen and Prof. Nagel, “hypothesis must be formulated in such a manner that deduction can be made from it and that consequently a decision can be reached as to whether it does or does not explain the facts considered.”

If the research work based on a hypothesis is to be successful, it is necessary that the later is as simple and easy as possible. An ambition of finding out something new may lead the researcher to frame an unrealistic and unclear hypothesis. Such a temptation is to be avoided. Framing a simple, easy and testable hypothesis requires that the researcher is well acquainted with the related concepts.

Sources of Hypothesis

Hypotheses can be derived from various sources. Some of the sources is given below:

Observation

State of knowledge, continuity of research.

Hypotheses can be derived from observation from the observation of price behavior in a market. For example the relationship between the price and demand for an article is hypothesized.

Analogies are another source of useful hypotheses. Julian Huxley has pointed out that casual observations in nature or in the framework of another science may be a fertile source of hypotheses. For example, the hypotheses that similar human types or activities may be found in similar geophysical regions come from plant ecology.

This is one of the main sources of hypotheses. It gives direction to research by stating what is known logical deduction from theory lead to new hypotheses. For example, profit / wealth maximization is considered as the goal of private enterprises. From this assumption various hypotheses are derived’.

An important source of hypotheses is the state of knowledge in any particular science where formal theories exist hypotheses can be deduced. If the hypotheses are rejected theories are scarce hypotheses are generated from conception frameworks.

Another source of hypotheses is the culture on which the researcher was nurtured. Western culture has induced the emergence of sociology as an academic discipline over the past decade, a large part of the hypotheses on American society examined by researchers were connected with violence. This interest is related to the considerable increase in the level of violence in America.

The continuity of research in a field itself constitutes an important source of hypotheses. The rejection of some hypotheses leads to the formulation of new ones capable of explaining dependent variables in subsequent research on the same subject.

Null and Alternative Hypothesis

Null hypothesis.

The hypothesis that are proposed with the intent of receiving a rejection for them are called Null Hypothesis . This requires that we hypothesize the opposite of what is desired to be proved. For example, if we want to show that sales and advertisement expenditure are related, we formulate the null hypothesis that they are not related.

Similarly, if we want to conclude that the new sales training programme is effective, we formulate the null hypothesis that the new training programme is not effective, and if we want to prove that the average wages of skilled workers in town 1 is greater than that of town 2, we formulate the null hypotheses that there is no difference in the average wages of the skilled workers in both the towns.

Since we hypothesize that sales and advertisement are not related, new training programme is not effective and the average wages of skilled workers in both the towns are equal, we call such hypotheses null hypotheses and denote them as H 0 .

Alternative Hypothesis

Rejection of null hypotheses leads to the acceptance of alternative hypothesis . The rejection of null hypothesis indicates that the relationship between variables (e.g., sales and advertisement expenditure) or the difference between means (e.g., wages of skilled workers in town 1 and town 2) or the difference between proportions have statistical significance and the acceptance of the null hypotheses indicates that these differences are due to chance.

As already mentioned, the alternative hypotheses specify that values/relation which the researcher believes hold true. The alternative hypotheses can cover a whole range of values rather than a single point. The alternative hypotheses are denoted by H 1 .

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What is falsifiability?

Falsifiability is the capacity for some proposition, statement, theory or hypothesis to be proven wrong. The concept of falsifiability was introduced in 1935 by Austrian philosopher and scientist Karl Popper (1902-1994). Since then, the scientific community has come to consider falsifiability to be one of the fundamental tenets of the scientific method , along with attributes such as replicability and testability.

A scientific hypothesis, according to the doctrine of falsifiability, is credible only if it is inherently falsifiable. This means that the hypothesis must be capable of being tested and proven wrong. It does not automatically mean that the hypothesis is invalid or incorrect, only that the potential exists for the hypothesis to be refuted at some possible time or place.

For example, one could hypothesize that a divine being with green scales, mauve hair, ochre-colored teeth and a propensity for humming show tunes rules over the physical universe from a different dimension. Even if millions of people were to swear their allegiance to such a being, there is no practical way to disprove this hypothesis, which means that it is not falsifiable. As a result, it cannot be considered a scientific assertion, according to the rules of falsifiability.

On the other hand, Einstein's theory of relativity is considered credible science according to these rules because it could be proven incorrect at some point in time through scientific experimentation and advanced testing techniques, especially as the methods continue to expand our body of knowledge. In fact, it's already widely accepted that Einstein's theory is at odds with the fundamentals of quantum mechanics, not unlike the way Newton's theory of gravity could not fully account for Mercury's orbit.

Another implication of falsifiability is that conclusions should not be drawn from simple observations of a particular phenomenon . The white swan hypothesis illustrates this problem. For many centuries, Europeans saw only white swans in their surroundings, so they assumed that all swans were white. However, this theory is clearly falsifiable because it takes the discovery of only one non-white swan to disprove its hypothesis, which is exactly what occurred when Dutch explorers found black swans in Australia in the late 17th century.

Falsifiability is often closely linked with the idea of the null hypothesis in hypothesis testing. The null hypothesis states the contrary of an alternative hypothesis. It provides the basis of falsifiability, describing what the outcome would demonstrate if the prediction of the alternative hypothesis is not supported. The alternative hypothesis might predict, for example, that fewer work hours correlates to lower employee productivity. A null hypothesis might propose that fewer work hours correlates with higher productivity or that there is no change in productivity when employees spend less time at work.

Popper makes the case for falsifiability

Karl Popper introduced the concept of falsifiability in his book The Logic of Scientific Discovery (first published in German in 1935 under the title Logik der Forschung ). The book centered on the demarcation problem, which explored the difficulty of separating science from pseudoscience . Popper claimed that only if a theory is falsifiable can it be considered scientific. In contrast, areas of study such as astrology, Marxism or even psychoanalysis were merely pseudosciences.

Popper's theories on falsifiability and pseudoscience have had a significant impact on what is now considered to be true science. Even so, there is no universal agreement about the role of falsifiability in science because of the limitations inherent in testing any hypothesis. Part of this comes from the fact that testing a hypothesis often brings its own set of assumptions, as well as an inability to account for all the factors that could potentially impact the outcome of a test, putting the test in question as much as the original hypothesis.

In addition, the tests we have at hand might be approaching their practical limitations when up against hypotheses such as string theory or multiple universes. It might not be possible to ever fully test such hypotheses to the degree envisioned by Popper. The question also arises whether falsifiability has anything to do with actual scientific discovery or whether the theory of falsification is itself falsifiable.

No doubt many researchers would argue that their brand of social or psychological science meets a set of criteria that is equally viable as those laid out by Popper. Even so, the important role that falsifiability has played in the scientific model cannot be denied, but Popper's black-and-white demarcation between science and pseudoscience might need to give way to a more comprehensive perspective of what we understand as being scientific.

See also:  empirical analysis ,  validated learning ,  OODA loop , black swan event,  deep learning .

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This is the Difference Between a Hypothesis and a Theory

What to Know A hypothesis is an assumption made before any research has been done. It is formed so that it can be tested to see if it might be true. A theory is a principle formed to explain the things already shown in data. Because of the rigors of experiment and control, it is much more likely that a theory will be true than a hypothesis.

As anyone who has worked in a laboratory or out in the field can tell you, science is about process: that of observing, making inferences about those observations, and then performing tests to see if the truth value of those inferences holds up. The scientific method is designed to be a rigorous procedure for acquiring knowledge about the world around us.

In scientific reasoning, a hypothesis is constructed before any applicable research has been done. A theory, on the other hand, is supported by evidence: it's a principle formed as an attempt to explain things that have already been substantiated by data.

Toward that end, science employs a particular vocabulary for describing how ideas are proposed, tested, and supported or disproven. And that's where we see the difference between a hypothesis and a theory .

A hypothesis is an assumption, something proposed for the sake of argument so that it can be tested to see if it might be true.

In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.

What is a Hypothesis?

A hypothesis is usually tentative, an assumption or suggestion made strictly for the objective of being tested.

When a character which has been lost in a breed, reappears after a great number of generations, the most probable hypothesis is, not that the offspring suddenly takes after an ancestor some hundred generations distant, but that in each successive generation there has been a tendency to reproduce the character in question, which at last, under unknown favourable conditions, gains an ascendancy. Charles Darwin, On the Origin of Species , 1859 According to one widely reported hypothesis , cell-phone transmissions were disrupting the bees' navigational abilities. (Few experts took the cell-phone conjecture seriously; as one scientist said to me, "If that were the case, Dave Hackenberg's hives would have been dead a long time ago.") Elizabeth Kolbert, The New Yorker , 6 Aug. 2007

What is a Theory?

A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, its likelihood as truth is much higher than that of a hypothesis.

It is evident, on our theory , that coasts merely fringed by reefs cannot have subsided to any perceptible amount; and therefore they must, since the growth of their corals, either have remained stationary or have been upheaved. Now, it is remarkable how generally it can be shown, by the presence of upraised organic remains, that the fringed islands have been elevated: and so far, this is indirect evidence in favour of our theory . Charles Darwin, The Voyage of the Beagle , 1839 An example of a fundamental principle in physics, first proposed by Galileo in 1632 and extended by Einstein in 1905, is the following: All observers traveling at constant velocity relative to one another, should witness identical laws of nature. From this principle, Einstein derived his theory of special relativity. Alan Lightman, Harper's , December 2011

Non-Scientific Use

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch (though theory is more common in this regard):

The theory of the teacher with all these immigrant kids was that if you spoke English loudly enough they would eventually understand. E. L. Doctorow, Loon Lake , 1979 Chicago is famous for asking questions for which there can be no boilerplate answers. Example: given the probability that the federal tax code, nondairy creamer, Dennis Rodman and the art of mime all came from outer space, name something else that has extraterrestrial origins and defend your hypothesis . John McCormick, Newsweek , 5 Apr. 1999 In his mind's eye, Miller saw his case suddenly taking form: Richard Bailey had Helen Brach killed because she was threatening to sue him over the horses she had purchased. It was, he realized, only a theory , but it was one he felt certain he could, in time, prove. Full of urgency, a man with a mission now that he had a hypothesis to guide him, he issued new orders to his troops: Find out everything you can about Richard Bailey and his crowd. Howard Blum, Vanity Fair , January 1995

And sometimes one term is used as a genus, or a means for defining the other:

Laplace's popular version of his astronomy, the Système du monde , was famous for introducing what came to be known as the nebular hypothesis , the theory that the solar system was formed by the condensation, through gradual cooling, of the gaseous atmosphere (the nebulae) surrounding the sun. Louis Menand, The Metaphysical Club , 2001 Researchers use this information to support the gateway drug theory — the hypothesis that using one intoxicating substance leads to future use of another. Jordy Byrd, The Pacific Northwest Inlander , 6 May 2015 Fox, the business and economics columnist for Time magazine, tells the story of the professors who enabled those abuses under the banner of the financial theory known as the efficient market hypothesis . Paul Krugman, The New York Times Book Review , 9 Aug. 2009

Incorrect Interpretations of "Theory"

Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.

The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)

This mistake is one of projection: since we use theory in general use to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.

The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."

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What is Hypothesis?

We have heard of many hypotheses which have led to great inventions in science. Assumptions that are made on the basis of some evidence are known as hypotheses. In this article, let us learn in detail about the hypothesis and the type of hypothesis with examples.

A hypothesis is an assumption that is made based on some evidence. This is the initial point of any investigation that translates the research questions into predictions. It includes components like variables, population and the relation between the variables. A research hypothesis is a hypothesis that is used to test the relationship between two or more variables.

Characteristics of Hypothesis

Following are the characteristics of the hypothesis:

• The hypothesis should be clear and precise to consider it to be reliable.
• If the hypothesis is a relational hypothesis, then it should be stating the relationship between variables.
• The hypothesis must be specific and should have scope for conducting more tests.
• The way of explanation of the hypothesis must be very simple and it should also be understood that the simplicity of the hypothesis is not related to its significance.

Sources of Hypothesis

Following are the sources of hypothesis:

• The resemblance between the phenomenon.
• Observations from past studies, present-day experiences and from the competitors.
• Scientific theories.
• General patterns that influence the thinking process of people.

Types of Hypothesis

There are six forms of hypothesis and they are:

• Simple hypothesis
• Complex hypothesis
• Directional hypothesis
• Non-directional hypothesis
• Null hypothesis
• Associative and casual hypothesis

Simple Hypothesis

It shows a relationship between one dependent variable and a single independent variable. For example – If you eat more vegetables, you will lose weight faster. Here, eating more vegetables is an independent variable, while losing weight is the dependent variable.

Complex Hypothesis

It shows the relationship between two or more dependent variables and two or more independent variables. Eating more vegetables and fruits leads to weight loss, glowing skin, and reduces the risk of many diseases such as heart disease.

Directional Hypothesis

It shows how a researcher is intellectual and committed to a particular outcome. The relationship between the variables can also predict its nature. For example- children aged four years eating proper food over a five-year period are having higher IQ levels than children not having a proper meal. This shows the effect and direction of the effect.

Non-directional Hypothesis

It is used when there is no theory involved. It is a statement that a relationship exists between two variables, without predicting the exact nature (direction) of the relationship.

Null Hypothesis

It provides a statement which is contrary to the hypothesis. It’s a negative statement, and there is no relationship between independent and dependent variables. The symbol is denoted by “H O ”.

Associative and Causal Hypothesis

Associative hypothesis occurs when there is a change in one variable resulting in a change in the other variable. Whereas, the causal hypothesis proposes a cause and effect interaction between two or more variables.

Examples of Hypothesis

Following are the examples of hypotheses based on their types:

• Consumption of sugary drinks every day leads to obesity is an example of a simple hypothesis.
• All lilies have the same number of petals is an example of a null hypothesis.
• If a person gets 7 hours of sleep, then he will feel less fatigue than if he sleeps less. It is an example of a directional hypothesis.

Functions of Hypothesis

Following are the functions performed by the hypothesis:

• Hypothesis helps in making an observation and experiments possible.
• It becomes the start point for the investigation.
• Hypothesis helps in verifying the observations.
• It helps in directing the inquiries in the right direction.

How will Hypothesis help in the Scientific Method?

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

• Formation of question
• Doing background research
• Creation of hypothesis
• Designing an experiment
• Collection of data
• Result analysis
• Summarizing the experiment
• Communicating the results

Frequently Asked Questions – FAQs

What is hypothesis.

A hypothesis is an assumption made based on some evidence.

Give an example of simple hypothesis?

What are the types of hypothesis.

Types of hypothesis are:

• Associative and Casual hypothesis

State true or false: Hypothesis is the initial point of any investigation that translates the research questions into a prediction.

Define complex hypothesis..

A complex hypothesis shows the relationship between two or more dependent variables and two or more independent variables.

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Hypothesis is a testable statement that explains what is happening or observed. It proposes the relation between the various participating variables. Hypothesis is also called Theory, Thesis, Guess, Assumption, or Suggestion. Hypothesis creates a structure that guides the search for knowledge.

In this article, we will learn what is hypothesis, its characteristics, types, and examples. We will also learn how hypothesis helps in scientific research.

Table of Content

What is Hypothesis?

Hypothesis meaning, characteristics of hypothesis, sources of hypothesis, types of hypothesis, simple hypothesis, complex hypothesis, directional hypothesis, non-directional hypothesis, null hypothesis (h0), alternative hypothesis (h1 or ha), statistical hypothesis, research hypothesis, associative hypothesis, causal hypothesis, hypothesis examples, simple hypothesis example, complex hypothesis example, directional hypothesis example, non-directional hypothesis example, alternative hypothesis (ha), functions of hypothesis, how hypothesis help in scientific research.

A hypothesis is a suggested idea or plan that has little proof, meant to lead to more study. It’s mainly a smart guess or suggested answer to a problem that can be checked through study and trial. In science work, we make guesses called hypotheses to try and figure out what will happen in tests or watching. These are not sure things but rather ideas that can be proved or disproved based on real-life proofs. A good theory is clear and can be tested and found wrong if the proof doesn’t support it.

A hypothesis is a proposed statement that is testable and is given for something that happens or observed.

• It is made using what we already know and have seen, and it’s the basis for scientific research.
• A clear guess tells us what we think will happen in an experiment or study.
• It’s a testable clue that can be proven true or wrong with real-life facts and checking it out carefully.
• It usually looks like a “if-then” rule, showing the expected cause and effect relationship between what’s being studied.

Here are some key characteristics of a hypothesis:

• Testable: An idea (hypothesis) should be made so it can be tested and proven true through doing experiments or watching. It should show a clear connection between things.
• Specific: It needs to be easy and on target, talking about a certain part or connection between things in a study.
• Falsifiable: A good guess should be able to show it’s wrong. This means there must be a chance for proof or seeing something that goes against the guess.
• Logical and Rational: It should be based on things we know now or have seen, giving a reasonable reason that fits with what we already know.
• Predictive: A guess often tells what to expect from an experiment or observation. It gives a guide for what someone might see if the guess is right.
• Concise: It should be short and clear, showing the suggested link or explanation simply without extra confusion.
• Grounded in Research: A guess is usually made from before studies, ideas or watching things. It comes from a deep understanding of what is already known in that area.
• Flexible: A guess helps in the research but it needs to change or fix when new information comes up.
• Relevant: It should be related to the question or problem being studied, helping to direct what the research is about.
• Empirical: Hypotheses come from observations and can be tested using methods based on real-world experiences.

Hypotheses can come from different places based on what you’re studying and the kind of research. Here are some common sources from which hypotheses may originate:

• Existing Theories: Often, guesses come from well-known science ideas. These ideas may show connections between things or occurrences that scientists can look into more.
• Observation and Experience: Watching something happen or having personal experiences can lead to guesses. We notice odd things or repeat events in everyday life and experiments. This can make us think of guesses called hypotheses.
• Previous Research: Using old studies or discoveries can help come up with new ideas. Scientists might try to expand or question current findings, making guesses that further study old results.
• Literature Review: Looking at books and research in a subject can help make guesses. Noticing missing parts or mismatches in previous studies might make researchers think up guesses to deal with these spots.
• Problem Statement or Research Question: Often, ideas come from questions or problems in the study. Making clear what needs to be looked into can help create ideas that tackle certain parts of the issue.
• Analogies or Comparisons: Making comparisons between similar things or finding connections from related areas can lead to theories. Understanding from other fields could create new guesses in a different situation.
• Hunches and Speculation: Sometimes, scientists might get a gut feeling or make guesses that help create ideas to test. Though these may not have proof at first, they can be a beginning for looking deeper.
• Technology and Innovations: New technology or tools might make guesses by letting us look at things that were hard to study before.
• Personal Interest and Curiosity: People’s curiosity and personal interests in a topic can help create guesses. Scientists could make guesses based on their own likes or love for a subject.

Here are some common types of hypotheses:

• Non-directional Hypothesis

Simple Hypothesis guesses a connection between two things. It says that there is a connection or difference between variables, but it doesn’t tell us which way the relationship goes.

Complex Hypothesis tells us what will happen when more than two things are connected. It looks at how different things interact and may be linked together.

Directional Hypothesis says how one thing is related to another. For example, it guesses that one thing will help or hurt another thing.

Non-Directional Hypothesis are the one that don’t say how the relationship between things will be. They just say that there is a connection, without telling which way it goes.

Null hypothesis is a statement that says there’s no connection or difference between different things. It implies that any seen impacts are because of luck or random changes in the information.

Alternative Hypothesis is different from the null hypothesis and shows that there’s a big connection or gap between variables. Scientists want to say no to the null hypothesis and choose the alternative one.

Statistical Hypotheis are used in math testing and include making ideas about what groups or bits of them look like. You aim to get information or test certain things using these top-level, common words only.

Research Hypothesis comes from the research question and tells what link is expected between things or factors. It leads the study and chooses where to look more closely.

Associative Hypotheis guesses that there is a link or connection between things without really saying it caused them. It means that when one thing changes, it is connected to another thing changing.

Causal Hypothesis are different from other ideas because they say that one thing causes another. This means there’s a cause and effect relationship between variables involved in the situation. They say that when one thing changes, it directly makes another thing change.

Following are the examples of hypotheses based on their types:

• Studying more can help you do better on tests.
• Getting more sun makes people have higher amounts of vitamin D.
• How rich you are, how easy it is to get education and healthcare greatly affects the number of years people live.
• A new medicine’s success relies on the amount used, how old a person is who takes it and their genes.
• Drinking more sweet drinks is linked to a higher body weight score.
• Too much stress makes people less productive at work.
• Drinking caffeine can affect how well you sleep.
• People often like different kinds of music based on their gender.
• The average test scores of Group A and Group B are not much different.
• There is no connection between using a certain fertilizer and how much it helps crops grow.
• Patients on Diet A have much different cholesterol levels than those following Diet B.
• Exposure to a certain type of light can change how plants grow compared to normal sunlight.
• The average smarts score of kids in a certain school area is 100.
• The usual time it takes to finish a job using Method A is the same as with Method B.
• Having more kids go to early learning classes helps them do better in school when they get older.
• Using specific ways of talking affects how much customers get involved in marketing activities.
• Regular exercise helps to lower the chances of heart disease.
• Going to school more can help people make more money.
• Playing violent video games makes teens more likely to act aggressively.
• Less clean air directly impacts breathing health in city populations.

Hypotheses have many important jobs in the process of scientific research. Here are the key functions of hypotheses:

• Guiding Research: Hypotheses give a clear and exact way for research. They act like guides, showing the predicted connections or results that scientists want to study.
• Formulating Research Questions: Research questions often create guesses. They assist in changing big questions into particular, checkable things. They guide what the study should be focused on.
• Setting Clear Objectives: Hypotheses set the goals of a study by saying what connections between variables should be found. They set the targets that scientists try to reach with their studies.
• Testing Predictions: Theories guess what will happen in experiments or observations. By doing tests in a planned way, scientists can check if what they see matches the guesses made by their ideas.
• Providing Structure: Theories give structure to the study process by arranging thoughts and ideas. They aid scientists in thinking about connections between things and plan experiments to match.
• Focusing Investigations: Hypotheses help scientists focus on certain parts of their study question by clearly saying what they expect links or results to be. This focus makes the study work better.
• Facilitating Communication: Theories help scientists talk to each other effectively. Clearly made guesses help scientists to tell others what they plan, how they will do it and the results expected. This explains things well with colleagues in a wide range of audiences.
• Generating Testable Statements: A good guess can be checked, which means it can be looked at carefully or tested by doing experiments. This feature makes sure that guesses add to the real information used in science knowledge.
• Promoting Objectivity: Guesses give a clear reason for study that helps guide the process while reducing personal bias. They motivate scientists to use facts and data as proofs or disprovals for their proposed answers.
• Driving Scientific Progress: Making, trying out and adjusting ideas is a cycle. Even if a guess is proven right or wrong, the information learned helps to grow knowledge in one specific area.

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

• Initiating Investigations: Hypotheses are the beginning of science research. They come from watching, knowing what’s already known or asking questions. This makes scientists make certain explanations that need to be checked with tests.
• Formulating Research Questions: Ideas usually come from bigger questions in study. They help scientists make these questions more exact and testable, guiding the study’s main point.
• Setting Clear Objectives: Hypotheses set the goals of a study by stating what we think will happen between different things. They set the goals that scientists want to reach by doing their studies.
• Designing Experiments and Studies: Assumptions help plan experiments and watchful studies. They assist scientists in knowing what factors to measure, the techniques they will use and gather data for a proposed reason.
• Testing Predictions: Ideas guess what will happen in experiments or observations. By checking these guesses carefully, scientists can see if the seen results match up with what was predicted in each hypothesis.
• Analysis and Interpretation of Data: Hypotheses give us a way to study and make sense of information. Researchers look at what they found and see if it matches the guesses made in their theories. They decide if the proof backs up or disagrees with these suggested reasons why things are happening as expected.
• Encouraging Objectivity: Hypotheses help make things fair by making sure scientists use facts and information to either agree or disagree with their suggested reasons. They lessen personal preferences by needing proof from experience.
• Iterative Process: People either agree or disagree with guesses, but they still help the ongoing process of science. Findings from testing ideas make us ask new questions, improve those ideas and do more tests. It keeps going on in the work of science to keep learning things.

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Summary – Hypothesis

A hypothesis is a testable statement serving as an initial explanation for phenomena, based on observations, theories, or existing knowledge. It acts as a guiding light for scientific research, proposing potential relationships between variables that can be empirically tested through experiments and observations.

The hypothesis must be specific, testable, falsifiable, and grounded in prior research or observation, laying out a predictive, if-then scenario that details a cause-and-effect relationship. It originates from various sources including existing theories, observations, previous research, and even personal curiosity, leading to different types, such as simple, complex, directional, non-directional, null, and alternative hypotheses, each serving distinct roles in research methodology .

The hypothesis not only guides the research process by shaping objectives and designing experiments but also facilitates objective analysis and interpretation of data , ultimately driving scientific progress through a cycle of testing, validation, and refinement.

Hypothesis – FAQs

What is a hypothesis.

A guess is a possible explanation or forecast that can be checked by doing research and experiments.

What are Components of a Hypothesis?

The components of a Hypothesis are Independent Variable, Dependent Variable, Relationship between Variables, Directionality etc.

What makes a Good Hypothesis?

Testability, Falsifiability, Clarity and Precision, Relevance are some parameters that makes a Good Hypothesis

Can a Hypothesis be Proven True?

You cannot prove conclusively that most hypotheses are true because it’s generally impossible to examine all possible cases for exceptions that would disprove them.

How are Hypotheses Tested?

Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data

Can Hypotheses change during Research?

Yes, you can change or improve your ideas based on new information discovered during the research process.

What is the Role of a Hypothesis in Scientific Research?

Hypotheses are used to support scientific research and bring about advancements in knowledge.

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What is a spouse? Here are the ATO's definitions and why it could affect your tax return

Do you live with your partner?

Even if you're not married, engaged or haven't been living together for all that long, in the eyes of the Australian Tax Office, that person is your spouse. 

And even if you still haven't introduced your partner to all your friends and family, the ATO wants you to declare them — and their income — on your tax return.

Here's what you need to know. 

Why does the ATO want to know?

Because having a spouse might affect how much tax you pay . 

"Having a spouse may impact the thresholds used for calculating the private health insurance rebate or liability for the Medicare Levy Surcharge, " the spokesperson says.

It can also include things like Medicare levy family reduction calculations and the ability to transfer senior and pensioner tax offset (SAPTO) to a spouse. 

What does 'spouse' mean on a tax return form?

It can mean someone you were in a relationship with and that relationship was registered under a prescribed state or territory law . 

But while many of us may use the word spouse as another term for husband or wife, the ATO's definition of spouse isn't just limited to legally married couples. 

It can also mean someone you were in a relationship with who you lived with as a couple for any period of time during the past financial year.

And that's the definition we'll be focusing on for the rest of this article. 

"Your spouse can be the same sex or a different sex, but they can't be a person who has another family relationship with you," an ATO spokesperson says. 

Does it matter how long you've been dating for?

"The duration of the relationship is one of the factors taken into account in determining if a relationship is a genuine relationship," the spokesperson said. 

But, the main thing the ATO cares about is how long you lived together last financial yea r — or how much of the financial year you were in a registered relationship with that person. 

Do you have to have been living together for a certain amount of time?

Whether you've lived together for the past 12 months or your partner only moved in a few days before the end of June, it still counts. 

If you haven't been living together for the whole year, there's an option to fill out how long you have lived together. 

What if you broke up?

If you lived together but that relationship was not registered, you only have to declare the amount of time you were living together. 

And an ATO spokesperson says there's help available for you. 

"If you are experiencing difficulty with tax obligations due to the circumstances of your relationship we do provide support," they said. 

They pointed to the ATO's support in difficult times portal and its personal crisis support website . 

What if I don't know my ex's income?

You can make an educated guess . 

"We understand that there may be situations where you are unable to find out your spouse's income for the purposes of including that information when lodging your tax return," an ATO spokesperson said. 

"In these situations, you can make a reasonable estimate. 

"You won't be penalised for an incorrect estimate if you acted reasonably and in good faith."

Can I change my tax return after I've filed it?

If you need to change a tax return within two years of getting your return back, you can make a request to do so by:

• going through the ATO's online portal
• going through your tax agent 
• sending a letter to the ATO

"We don't charge a fee if you request an amendment and you don't have to send in another tax return unless we ask you to," the ATO's website says . 

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Defining Yourself: The Importance of Relationships

Defining yourself starts with getting to know your own thoughts, values, and ideals..

Posted July 29, 2024 | Reviewed by Michelle Quirk

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• Finding a balance between being your own person and being close to others is a fulfilling way to live.
• Defining yourself involves aligning your behaviors with your values, ideals, and goals.

​​​​​​ It's a common belief that one must be alone or single to truly understand oneself. While there's some truth to this, the process of defining oneself is deeply intertwined with family dynamics. Personal growth often involves taking actionable steps in relationships with emotionally significant others, such as family members.

Defining yourself refers to developing a clear sense of self while maintaining healthy emotional connections with others. It's about finding a balance between being your own person and being close to others. It involves expressing your thoughts and feelings without losing your sense of identity in relationships. For example, a person may learn to voice their opinions during family discussions, asserting their perspective even when it differs from the majority. This ability to communicate honestly fosters personal growth and strengthens relationships by promoting understanding and respect for differing viewpoints.

When we are emotionally triggered, our feelings become intertwined with those of others, and it influences us to act in ways we don't like or say things we don't mean, especially when we are stressed or anxious. When we react while triggered, we behave in ways that are counterproductive to our values and best interests. When we are emotionally reactive, our judgment is clouded, and it influences how we see the world and ourselves.

Defining yourself involves aligning your behaviors with your values, ideas, and goals . This cannot happen when you rely on automatic "go-to" moves to relieve anxiety in your relationships. Relying on automatic "go-to" moves to alleviate anxiety in relationships refers to habitual reactions or coping mechanisms that individuals fall back on when faced with stress or emotional discomfort. These responses, often unconscious , can manifest in various forms, such as avoidance, defensiveness, or over-apologizing. While these behaviors might provide temporary relief from anxiety, they can hinder genuine communication and deepen relational issues.

For instance, someone might instinctively withdraw when conflict arises, avoiding difficult conversations that could lead to resolution and understanding. This pattern prevents individuals from truly addressing their feelings or needs and may result in miscommunication, resentment, or emotional disconnection. In essence, relying on such automatic responses limits personal growth and the opportunity to create more fulfilling relationships.

Defining yourself starts with internal learning—getting to know your own thoughts, values, and ideas—then integrating what you've learned by acting in ways that align with it. An example of defining yourself through self-discovery could be the journey of an individual who values honesty and authenticity . Initially, they might notice a tendency to agree with others to avoid conflict, a behavior rooted in fear of rejection. Through introspection, they begin to recognize that suppressing their true opinions causes internal discomfort and impacts their relationships.

They realize they deeply value open and honest communication by exploring their thoughts. Armed with this understanding, they practice expressing their viewpoints genuinely, even in challenging situations. For instance, during a team meeting, they voice their concerns respectfully instead of remaining silent about a disagreement on a project approach. This shift aligns their actions with their values and fosters a more authentic connection with their colleagues, ultimately leading to improved collaboration and mutual respect. This process of self-awareness and alignment allows them to define themselves more clearly.

Additionally, defining yourself within your family is essential to defining yourself in other relationships. Understanding your family patterns can help you better differentiate yourself and work toward improving your part within your family and other relationship networks. This can involve setting boundaries , expressing your needs and feelings, and learning to communicate openly and honestly.

What Does It Mean to Define Yourself in Your Family?

Family relationships have a significant impact on how people develop and how they think about themselves. Perhaps that is why we all want the picture-perfect family—a family we connect with and feel on the same page with. A family in which all members agree on religion, politics , and lifestyle choices. A family that doesn't trigger us or make us question ourselves. A family that always has our back gives us the benefit of the doubt and allows us to be who we are without judgment.

That type of family does sound nice. However, have you ever seen that kind of family in real life? Every family is a lot messier and more complex than it looks from the outside. Seldom do we get the parents we dream of, the children we planned for, or the sibling relationships we see in movies. Sometimes, even a loving family can bring us more pain than joy.

Defining yourself within your family is a liberating process. It empowers you to break free from the habitual emotional processes of your family while maintaining a strong connection. When you can bond with others without losing connection to yourself, you gain the ability to reflect on a fight or argument, understand your part in it, and choose a new, more constructive way to respond.

If you grew up in a family where everyone maintains tight closeness despite having different thoughts and feelings—and even brief arguments—you likely find it easier to define yourself. Alternately, if your family's mantra was "Being close means agreeing on everything" or "It's my way or the highway," defining yourself is likely very difficult.

Every person possesses a division within themselves. We have our solid selves, with our own ideas and values, and we have other points of view that we've internalized from our family, society, and so on. When we feel anxious or upset, we instinctively alter our thinking to better fit our family patterns.

Recognizing what triggers you to revert to old thoughts and behavior patterns is a crucial step in the journey of self-definition. It equips you with the awareness and understanding needed to either continue those patterns or make choices that allow for a more solid, emotionally mature self.

Ilene S. Cohen, Ph.D. , is a psychotherapist and blogger, who teaches in the Department of Counseling at Barry University.

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HYPOTHESIS AND THEORY article

From diagnosis to dialogue – reconsidering the dsm as a conversation piece in mental health care: a hypothesis and theory.

• 1 Department of Psychiatry, Utrecht University Medical Center, Utrecht, Netherlands
• 2 Digital Innovation in Health, NHL Stenden University of Applied Sciences, Leeuwarden, Netherlands
• 3 Department of Research and Innovation, KieN VIP Mental Health Care Services, Leeuwarden, Netherlands
• 4 Department of Child and Family Welfare, University of Groningen, Groningen, Netherlands

The Diagnostic and Statistical Manual of Mental Disorders, abbreviated as the DSM, is one of mental health care’s most commonly used classification systems. While the DSM has been successful in establishing a shared language for researching and communicating about mental distress, it has its limitations as an empirical compass. In the transformation of mental health care towards a system that is centered around shared decision-making, person-centered care, and personal recovery, the DSM is problematic as it promotes the disengagement of people with mental distress and is primarily a tool developed for professionals to communicate about patients instead of with patients. However, the mental health care system is set up in such a way that we cannot do without the DSM for the time being. In this paper, we aimed to describe the position and role the DSM may have in a mental health care system that is evolving from a medical paradigm to a more self-contained profession in which there is increased accommodation of other perspectives. First, our analysis highlights the DSM’s potential as a boundary object in clinical practice, that could support a shared language between patients and professionals. Using the DSM as a conversation piece, a language accommodating diverse perspectives can be co-created. Second, we delve into why people with lived experience should be involved in co-designing spectra of distress. We propose an iterative design and test approach for designing DSM spectra of distress in co-creation with people with lived experience to prevent the development of ‘average solutions’ for ‘ordinary people’. We conclude that transforming mental health care by reconsidering the DSM as a boundary object and conversation piece between activity systems could be a step in the right direction, shifting the power balance towards shared ownership in a participation era that fosters dialogue instead of diagnosis.

1 Introduction

The Diagnostic Statistical Manual of Mental Disorders (DSM) has great authority in practice. The manual, released by the American Psychiatric Association (APA), provides a common language and a classification system for clinicians to communicate about people’s experiences of mental distress and for researchers to study social phenomena that include mental distress and its subsequent treatments. Before the DSM was developed, a plethora of mental health-related documents circulated in the United States ( 1 ). In response to the confusion that arose from this diversity of documents, the APA Committee on Nomenclature and Statistics standardized these into one manual, the DSM-I ( 2 ). In this first edition of the manual, released in 1952, mental distress was understood as a reaction to stress caused by psychological and interpersonal factors in the person’s life ( 3 ). Although the DSM-I had limited impact on practice ( 4 ), it did set the stage for increasingly standardized categorization of mental disorders ( 5 ).

The DSM-II was released in 1968. In this second iteration, mental disorders were understood as the patient’s attempts to control overwhelming anxiety with unconscious, intrapsychic conflicts ( 3 ). In this edition, the developers attempted to describe the symptoms of disorders and define their etiologies. They had chosen to base them predominantly on psychodynamic psychiatry but also included the biological focus of Kraepelin’s system of classification ( 5 , 6 ). During the development of the DSM-III, the task force added the goal to improve the reliability — the likelihood that different professionals arrive at the same diagnosis — of psychiatric diagnosis, which now became an important feature of the design process. The developers abandoned the psychodynamic view and shifted the focus to atheoretical descriptions, aiming to specify objective criteria for diagnosing mental disorders ( 3 ). Although it was explicitly stated in DSM-III that there was no underlying assumption that the categories were validated entities ( 7 ), the categorical approach still assumed each pattern of symptoms in a category reflected an underlying pathology. The definition of ‘mental illness’ was thereby altered from what one did or was (“you react anxious/you are anxious”) to something one had (“you have anxiety”). This resulted in descriptive, criteria-based classifications that reflected a perceived need for standardization of psychiatric diagnoses ( 5 , 6 ). The DSM-III was released in 1980 and had a big impact on practice ( 6 ) as it inaugurated an attempt to “re-medicalize” American psychiatry ( 5 ).

In hindsight, it is not surprising that after the release of the DSM-III, the funding for psychopharmacological research skyrocketed ( 8 ). At the same time, the debate on the relationship between etiology and description in psychiatric diagnosis continued ( 9 ). As sociologist Andrew Scull ( 10 ) showed, the election of President Reagan prompted a shift towards a focus on biology. His successor, President Bush, claimed that the 1990s were ‘the decade of the brain,’ which fueled a sharp increase in funding for research on genetics and neuroscience ( 10 ). Despite the public push for biological research, the DSM-IV aimed to arrive at a purely atheoretical description of psychiatric diagnostic criteria and was released in 1994 ( 11 ). The task force conducted multi-center field trials to relate diagnoses to clinical practice to improve reliability, which remained a goal of the design process ( 12 ). While the DSM-IV aimed to be atheoretical, researchers argued that the underlying ontologies were easily deducible from their content: psychological and social causality were eliminated and replaced implicitly with biological causality ( 13 ). In the DSM-5, validity — whether a coherent syndrome is being measured and whether it is what it is assumed to be — took center stage ( 10 ). The definition of mental disorder in the DSM-5 was thereby conceptualized as:

“… a syndrome characterized by clinically significant disturbance in an individual’s cognition, emotion regulation, or behavior that reflects a dysfunction in the psychological, biological, or developmental processes underlying mental functioning.” ( 14 ).

With the release of the DSM-5, the debate surrounding the conceptualization of mental distress started all over again, but this can be best seen as re-energizing longstanding debates around the utility and validity of APA nosology ( 15 ). Three important design goals from the DSM-III until current editions can be observed: providing an international language on mental distress, developing a reliable classification system, and creating a valid classification system.

1.1 The limitations of the DSM as an empirical compass

The extent to which these three design goals were attained is only partial. The development of an international language has been accomplished, as the DSM (as well as the International Classification of Diseases) is now widely employed across most Western countries. Although merely based on consensus, the DSM enables — to an extent — professionals and researchers to quantify the prevalence of certain behaviors and find one or more classifications that best suit these observed behaviors. To this date, the expectation that diagnostic criteria would be empirically validated through research has not yet been fulfilled ( 10 , 16 , 17 ). As stated by the authors of the fourth edition ( 11 ), the disorders listed in the DSM are “valuable heuristic constructs” that serve a purpose in research and practice. However, it was already emphasized in the DSM-IV guidebook that they do not precisely depict nature as it is, being characterized as not “well-defined entities” ( 18 ). Furthermore, while the fifth edition refers to “syndromes,” it is again described that “there is no assumption that each category of mental disorder is a completely discrete entity with absolute boundaries dividing it from other mental disorders or from no mental disorder” ( 14 ). Consequently, there are no laboratory tests or biological markers to set the boundary between ‘normal’ and ‘pathological,’ thus, it cannot confirm or reject the presumed pathologies underlying the DSM classifications, thereby rendering the validity goal of the design unattained. Therefore, the reliability of the current major DSM (i.e., DSM-5) still raises concerns ( 19 ).

By focusing conceptually on mental distress as an individual experience, the DSM task forces have neglected the role of social context, potentially restricting a comprehensive clinical understanding of mental distress ( 20 ). There is mounting evidence and increased attention, however, that the social environment, including its determinants and factors, is crucial for the onset, course, and outcome of mental distress ( 21 – 27 ). Moreover, exposure to factors such as early life adversity, poverty, unemployment, trauma, and minority group position is strongly associated with the onset of mental distress ( 28 , 29 ). It is also established that the range of ontological perspectives — what mental distress is and how it exists — is far broader than what is typically covered in prevailing scientific and educational discussions ( 30 ). These diverse perspectives are also evident in the epistemic pluralism among theoretical models on mental health problems ( 31 ).

1.2 The DSM is problematic in the transformation of mental health care

In the context of contemporary transformations in mental health care, the role of the DSM as an empirical instrument becomes even more problematic. In recent years, significant shifts have been witnessed in mental health care services, with a growing focus on promoting mental well-being, preventive measures, and person-centered and rights-based approaches ( 32 ). In contrast to the 1950s definition of health in which health was seen as the absence of disease, health today is defined as “the ability to adapt and to self-manage” ( 33 ), also known as ‘positive health.’ Furthermore, the recovery movement ( 34 ), person-centered care ( 35 ), and the integration of professionals’ lived experiences ( 36 ) all contributed to a more person-centered mental health care that promotes shared-decision making as a fundamental principle in practice in which no one perspective holds the wisdom. Shared decision-making is “an approach where clinicians and patients share the best available evidence when faced with the task of making decisions, and where patients are supported to consider options, to achieve informed preferences” ( 37 ). To realize and enable a more balanced relationship between professional and patient in shared decision-making, the interplay of healthcare professionals’ and patients’ skills, the support for a patient, and a good relationship between professional and patient are important to facilitate patients’ autonomy ( 38 ). Thus, mental health care professionals in the 21st century should collaborate, embrace ideography, and maximize effects mediated by therapeutic relationships and the healing effects of ritualized care interactions ( 39 ).

The DSM and its designed classifications, as well as their use in the community, can hinder a person-centered approach in which meaning is collaboratively derived for mental health issues, where a balanced relationship is needed, and where decisions are made together. We can demonstrate this with a brief example involving the ADHD classification and its criteria, highlighting how its design tends to marginalize individuals with mental distress, reducing their behavior to objectification from the clinician’s viewpoint. The ADHD classification delineates an ideal self that highly esteems disengagement from one’s feelings and needs, irrespective of contextual factors ( 40 ). This inclination is apparent in the criteria, including criterion 1a concerning inattention: “often avoids, dislikes, or is reluctant to engage in tasks that require sustained mental effort”. This indicates that disliking something is viewed as a symptom rather than a personal preference ( 40 ). Due to a lack of attention to the person’s meaning, a behavior that may be a preference of the individual can become a symptom of a disease. Another instance can be observed in criterion 2c: “often runs about or climbs in situations where it is inappropriate.” Although such behavior might be deemed inappropriate in certain contexts, many individuals derive enjoyment from running and climbing. In this way, ‘normal’ human behavior can be pathologized because there is no room for the meaning of the individual.

A parallel disengagement is evident in the DSM’s viewpoint on individuals with mental distress ( 40 ), as the diagnostic process appears to necessitate no interaction with an individual; instead, it fosters disengagement rather than engagement. For example, according to the DSM-5, when a child is “engaged in especially interesting activities,” the clinician is warned that the ‘symptoms’ may not manifest. Although it appears most fitting to assist the child by exploring their interests, clinicians are instead encouraged to seek situations the child finds uninteresting and assess whether the child can concentrate ( 40 ). If the child cannot concentrate, a ‘diagnosis’ might be made, and intervention can be initiated. This highlights that the design of the DSM promotes professionals to locate individual disorders in a person at face value without considering contextual factors, personal preferences, or other idiosyncrasies in a person’s present or history ( 41 ). It is also apparent that the term ‘symptom’ in the DSM implies an underlying entity as its cause, obscuring that it is a subjective criterion based on human assessment and interpretation ( 42 ). These factors make it difficult for the DSM in its current form to have a place in person-centered mental health care that promotes shared decision-making.

1.3 The problem and hypotheses

Diagnostic manuals like the DSM function similarly to standard operating procedures: they streamline decision-making and assist professionals in making approximate diagnoses when valid and specific measures are lacking or not readily accessible ( 43 ). However, the DSM is often (mis)used as a manual providing explanations for mental distress. This hinders a personalized approach that prioritizes the patient’s needs. Furthermore, this approach does not align with the principles of shared decision-making, as the best available evidence indicates that classifications are not explanations for mental distress. Also, disengagement is promoted in the design of the DSM, which is problematic in the person-centered transformation of mental health care in which a range of perspectives and human-centered interventions are needed. This paper aims to describe the position and role the DSM may have in a mental health care system that is evolving from a medical paradigm to a more self-contained profession in which there is increased accommodation of other perspectives. For this hypothesis and theory paper, we have formulated the following hypotheses:

(1) Reconsidering the DSM as a boundary object that can be used as a conversation piece allows for other perspectives on what is known about mental distress and aligns with the requirements of person-centered mental health care needed for shared decision-making;.

(2) Embracing design approaches in redesigning the DSM to a conversation piece that uses spectra of mental distress instead of classifications will stimulate the integration of diverse perspectives and voices in reshaping mental health care.

2 Co-creation of a real common language

The DSM originally aimed to develop a common language, and it has achieved that to some extent, but it now primarily serves as a common language among professionals. This does not align with the person-centered transformation in mental health care, where multiple perspectives come into play ( 32 , 44 ). In this section, we will address our first hypothesis: reconsidering the DSM as a boundary object that can be used as a conversation piece allows for other perspectives on what is known about mental distress and aligns with the requirements of person-centered mental health care needed for shared decision-making. First, we will examine several unintended consequences of classifications. After that, we propose considering the DSM as boundary objects to arrive at a real common language in which the perspective of people with lived experience is promoted. This perspective views the DSM as a conversation piece that can be used as a subject, the meaning of which can be attributed from various perspectives where the premise is that there is not an omniscient perspective.

2.1 Validation, stigma, and making up people

Classifications influence what we see or do not see, what is valorized, and what is silenced ( 45 ). DSM classifications and the process of getting them can provide validation and relief for some service users, while for others, it can be stigmatizing and distressing ( 46 , 47 ). The stigma people encounter can be worse than the mental problems themselves ( 48 ). The classification of people’s behaviors is not simply a passive reflection of pre-existing characteristics but is influenced by social and cultural factors. The evolution of neurasthenia serves as a fascinating illustration of the notable ontological changes in the design of the DSM, constantly reflecting and constructing reality. Initially, neurasthenia was considered a widespread mental disorder with presumed somatic roots. Still, it was subsequently discarded from use, only to resurface several decades later as a culture-bound manifestation of individual mental distress ( 49 ). Consequently, certain mental disorders, as depicted in the DSM, may not have existed in the same way as before the classifications were designed. This has been called ‘making up people’, which entails the argument that different kinds of human beings and human acts come into being hand in hand with our invention of the categories labeling them ( 50 ). Furthermore, it is important to consider that whether behavior is deemed dysfunctional or functional is always influenced by the prevailing norms and traditions within a specific society at a given time. Therefore, the individual meaning of the patient in its context is always more important than general descriptions and criteria of functional and dysfunctional behavior (i.e., ADHD climbing example).

Individuals might perceive themselves differently and develop emotions and behaviors partly due to the classifications imposed upon them. Over time, this can result in alterations to the classification itself, a phenomenon referred to as the classificatory looping effect ( 51 ). Moreover, when alterations are made to the world that align with the system’s depiction of reality, ‘blindness’ can occur ( 45 ). To illustrate, let’s consider an altered scenario of Bowker and Star ( 45 ) in which all mental distress is categorized solely based on physiological factors. In this context, medical frameworks for observation and treatment are designed to recognize physical manifestations of distress, such as symptoms, and the available treatments are limited to physical interventions, such as psychotropic medications. Consequently, in such a design, mental distress may solely be a consequence of a chemical imbalance in the brain, making it nearly inconceivable to consider alternative conceptualizations or solutions. Thus, task forces responsible for designing mental disorder classifications should be acutely aware that they actively contribute to the co-creation of reality with the classifications they construct upon reality ( 49 ).

2.2 Reification and disorderism

Another unintended consequence is the reification of classifications. Reification involves turning a broad and potentially diverse range of human experiences into a fixed and well-defined category. Take, for example, the case of the classification of ADHD and its reification mechanisms (i.e., language choice, logical fallacies, genetic reductionism, and textual silence) ( 42 ). Teachers sometimes promote the classification of ADHD as they believe it acknowledges a prior feeling that something is the matter with a pupil. The classification is then seen as a plausible explanation for the emergence of specific behaviors, academic underperformance, or deviations from the expected norm within a peer group ( 52 , 53 ). At first glance, this may seem harmless. However, it reinforces the notion that a complex and multifaceted set of contextual behaviors, experiences, and psychological phenomena are instead a discrete, objective entity residing in the individual. This is associated with presuppositions in the DSM that are not explicitly articulated, such as attributing a mental disorder to the individual rather than the system, resulting in healthcare that is organized around the individual instead of organized around the system ( 54 ).

In this way, DSM classifications can decontextualize mental distress, leading to ‘disorderism’. Disorderism is defined as the systemic decontextualization of mental distress by framing it in terms of individual disorders ( 55 ). The processes by which people are increasingly diagnosed and treated as having distinct treatable individual disorders, exemplified by the overdiagnosis of ADHD in children and adolescents ( 56 ), while at the same time, the services of psychiatry shape more areas of life, has been called the ‘psychiatrization of society’ ( 57 ). The psychiatrization of society encompasses a pervasive influence whereby the reification and disorderism extend beyond clinical settings and infiltrate various facets of daily life. It is a double-edged sword that fosters increased awareness of mental health issues and seeks to reduce stigma, but at the same time, raises concerns about the overemphasis on medical models, potentially neglecting the broader social, cultural, and environmental factors that contribute to individual well-being as well as population salutogenesis ( 58 ).

2.3 The DSM as a boundary object between activity systems in clinical practice

Instead of using the DSM as a scientific and professional tool in order to classify, the DSM can be reconsidered as a boundary object. When stakeholders with different objectives and needs have to work together constructively without making concessions, like patients and professionals in person-centered mental health care, objects can play a bridging role. Star and Griesemer ( 59 ) introduced the term boundary objects for this purpose.

“Boundary objects are objects that are plastic enough to adapt to the local needs and constraints of the different parties using them, yet robust enough to maintain a common identity in different locations. They are weakly structured in common use and become strongly structured in use in individual locations. They can be abstract or concrete. They have different meanings in different social worlds, but their structure is common enough to more than one world to make them recognizable, a means of translation.” ( 59 ).

Before exploring the benefits of a boundary object perspective for the DSM, it is important to note that it remains questionable whether the DSM in its current form can help establish a shared understanding or provide diagnostic, prognostic, or therapeutic value ( 60 – 63 ). To make the DSM more suitable for accommodating different perspectives and types of knowledge, the DSM task force can focus its redesign on leaving the discrete disease entities — which classifications imply — behind by creating spectra. This way of thinking has already found its way to the DSM-5, in which mental distress as a spectrum was introduced in the areas of autism, substance use, and nearly personality disorders, and following these reconceptualization, also a psychosis spectrum was proposed ( 43 ), but this proposition was eventually not adopted in the manual. As mental distress can be caused by an extensive range of factors and mechanisms that result from interactions in networks of behaviors and patterns that have complex dynamics that unfold over time ( 64 ), spectra of mental distress may be more suitable for conversations about an individual’s narrative and needs in clinical practice, as each experience of mental distress is unique and contextual.

If the DSM is reconsidered as a boundary object that is intended to provide a shared language for interpreting mental distress while addressing the unintended consequences of classifications, it is also essential to consider where this language now primarily manifests itself, how it relates to shared decision-making, and the significant role it plays for patients in the treatment process. In recent decades, the DSM has positioned itself primarily as a professional tool for clinical judgment (see Figure 1 ). In this way, professionals have more or less acquired a monopoly on the language of classifications and the associated behaviors and complaints described in the DSM. It provides professionals with a tool to pursue their professional objectives and legitimacy for their professional steps with patients, resulting in a lack of equality from which different perspectives can be examined side by side. However, with shared decision-making, patients are expected to be engaged and to help determine the course of treatment; the language surrounding classifications and symptoms does not currently allow that to happen sufficiently.

Figure 1 DSM as a professional tool, adapted from Figure 1, ‘Design of a Digital Comic Creator (It’s Me) to Facilitate Social Skills Training for Children With Autism Spectrum Disorder: Design Research Approach’, by Terlouw et al., CC-BY ( 65 ).

This is where boundary objects come into play. The focused shaping of boundary objects can ensure a more equal role for different stakeholders ( 65 – 67 ). Boundary objects can also trigger perspective-making and -taking from a reflective dialogical learning mechanism ( 68 – 70 ), which ensures a better shared understanding of all perspectives. Boundary objects and their dialogical learning mechanisms also align well with co-design ( 71 ). If we consider the DSM a boundary object, it positions itself between the activity system of the professionals, patients, and other people close to the patient ( Figure 2 ). The boundary between activity systems represents not only the cultural differences and potential challenges in actions and interactions but also the significant value in establishing communication and collaboration ( 71 ). All sides can give meaning to the DSM language from their perspective. By effectively considering the DSM as a boundary object, the DSM serves as a conversation piece—a product that elicits and provides room for questions and comments from other people, literally one that encourages conversation ( 72 ). As a conversation piece rather than a determinative classification system, it can contribute to mapping the meaning of complaints, behaviors, signs, and patterns for different invested parties. It also provides space for the patient’s contextual factors, subjective experience, needs, and life events, which are essential to giving constructive meaning to mental distress. This allows for interpretative flexibility; professionals can structure their work, while patients can give meaning to their subjective experience of mental distress.

Figure 2 DSM as a boundary object, adapted from Figure 1, ‘Design of a Digital Comic Creator (It’s Me) to Facilitate Social Skills Training for Children With Autism Spectrum Disorder: Design Research Approach’, by Terlouw et al., CC-BY ( 65 ).

As the DSM as a boundary object enables interpretative flexibility, it could then be used to enact conversations and develop a shared understanding in partnership between the patient and the professional; patients are no longer ‘diagnosed’ with a disorder from a professional point of view. It is important to note that the conceptual history of understanding the diagnostic process as essentially dialogical and not as a merely technical-quantitative procedure was already started in the early 1900s. For example, in the 1913 released ‘General Psychopathology,’ Karl Jaspers presented a phenomenological and comprehensive perspective for psychiatry with suggestions about how to understand the psychopathological phenomena as experienced by the patient through empathic understanding, allowing to understand the patient’s worldview and existential meanings ( 73 ). A century after its first publication, academics continue to leverage Jaspers’ ideas to critique modern operationalist epistemology ( 74 ). Following the notion of the diagnostic process as a dialogical one, the reconsideration of the DSM as a boundary object could accommodate the patient’s idiographic experience and the professional’s knowledge about mental distress by using these potential spectra as conversation pieces, shifting the power balance in clinical practice towards co-creation and dialogue. The spectra can then be explained as umbrella terms that indicate a collection of frequently occurring patterns and signs that can function as a starting point for a co-creative inquiry that promotes dialogue, aligning more with current empirical evidence of lived experience than using classifications as diagnoses.

Considering the advantages and strengths boundary objects bring to a mental health care system centered around shared decision-making and co-creation, the DSM could be a boundary object that is interpreted from various perspectives. Take, for example, altered perceptions, which is a characteristic commonly seen in people who receive a psychosis-related classification in clinical care. For some, these perceptions have person-specific meaning ( 75 , 76 ). By using the DSM as a boundary object and as a conversation piece, the patient and professional can give meaning by using the spectra in the manual as a starting point for a common language instead of using a classification to explain the distress. This requires a phenomenological and idiographic approach considering person-specific meaning and idiosyncrasies. Consequently, diagnostic practices should be iterative to align with the dynamic circumstances, with the individual’s narrative taking center stage in co-creation between professional and patient ( 41 , 49 ), as this reconsidered role fosters the engagement instead of the disengagement of patients. Additionally, the potential role of the DSM as a boundary object and conversation piece may also have a positive effect on societal and scientific levels, specifically on how mental distress is perceived and conceptualized. It can ‘systemically contextualize’ mental distress, which could eliminate the disorderism and the psychiatrization of society, and in the end, hopefully, contribute to population salutogenesis.

3 Co-design of DSM spectra of mental distress

If the DSM is reconsidered as a conversation piece in which spectra of mental distress replace classifications, it is important to address that these must be co-designed to accommodate diverse stakeholder perspectives and various types of knowledge side by side in clinical practice. Therefore, developers and designers need to embrace lived experience in the co-development of these spectra of mental distress to ensure patients’ engagement in clinical practice, as the patient effectively becomes a stakeholder of the DSM. This requires a different approach and procedure than DSM task forces used in past iterations. In this section, we will address our second hypothesis: embracing design approaches in redesigning the DSM to a conversation piece that uses spectra of mental distress instead of classifications will stimulate the integration of diverse perspectives and voices in reshaping mental health care. While we focus a little on the what (spectra of mental distress), we mainly focus on the how (the procedure that could be followed to arrive at the what). First, we will discuss the importance of lived experience leadership in design and research. Second, we argue that in the conceptual co-design of DSM spectra, lived experience leadership can be a way forward. Third, we take the stance that a designerly way of thinking and doing can shift the premature overcommitment task forces had to iterative exploration. In the concluding paragraph, we propose a design procedure that embraces engagement and iteration as core values for developing robust and flexible spectra of mental distress that are meaningful for service users and professionals.

3.1 Lived experience leadership and initiatives in design and research

First, let us briefly examine the evolution of lived experience in design and science over time to provide context for why engaging people with lived experience in the design of spectra of mental distress is important for innovation. Since 1960, people with lived experiences have tried to let their voices be heard, but initially to no avail, and their civil rights movement of reformist psychiatry was labeled as ‘anti-psychiatry’ ( 77 ). During the turn of the millennium, lived experience received increased recognition and eventually became an important pillar of knowledge that informed practice and continues to do so on various levels of mental health care ( 34 , 36 , 78 – 81 ). While there is currently growing attention to the perspective of lived experience in, for example, mental health research ( 79 , 80 , 82 , 83 ) and mental health care design and innovation ( 84 – 90 ), overall, their involvement remains too low in the majority of research and design projects ( 88 , 91 , 92 ). While there has been a significant increase in the annual publication of articles claiming to employ collaborative methods with people with lived experience, these studies often use vague terms to suggest a higher engagement level than is the case ( 93 ). This has led to initiatives such as that of The Lancet Psychiatry to facilitate transparent reporting of lived experience work ( 93 , 94 ).

Although the involvement of people with lived experience and its reporting needs attention in order to prevent tokenism and co-optation ( 89 ), some great user-driven initiatives resulted in innovative design and research that improved mental health care and exemplifies why their engagement should be mandatory. The Co-Design Living Labs is such an initiative. Its program exemplifies an adaptive and embedded approach for people with lived experiences of mental distress to drive mental health research design to translation ( 95 ). In this community-based approach, people with lived experience, their caregivers, family members, and support networks collaboratively drive research with university researchers, which is very innovative considering the relatively low engagement of people with lived experience in general mental health research. Another example is the development of person-specific tapering medication initiated by people with lived experience of withdrawal symptoms. People with lived experience began to devise practical methods to discontinue medications on their own safely because of the lack of a systematic and professional response to severe and persistent withdrawal. This resulted in the accumulation of experience-based knowledge about withdrawal, ultimately leading to co-creating what is now known as tapering strips ( 81 ). The development of these tapering strips shows that people with lived experience have novel experience-based ideas for design and research that can result in human-centered innovation. Both examples underline the importance of human-centered design in which people with lived experience and knowledge are taken seriously and why the participation era requires that individuals with lived experience are decision-makers from the project’s start to produce novel perspectives for innovative design and research ( 88 , 93 ).

3.2 The conceptual co-design of DSM spectra of mental distress and the potential of integrating lived experiences

Engaging people with lived experience of mental distress in redesigning the DSM towards a spectrum-based guideline is of special importance, albeit a more conceptual design task in comparison to the earlier examples. What mental distress is remains a fundamental philosophical and ontological question that should be addressed in partnership as it sits at the core of how mental health care is organized. To allow novel ontologies to reach their full potential and act as drivers of a landscape of promising innovative scientific and clinical approaches, investment is required in development and elaboration ( 30 ). This, as well as the epistemic pluralism among theoretical models on mental health problems ( 31 ), makes it evident there is currently not one coherent accepted explanation or consensus on what mental distress is and how it exists. Without clear etiological understanding, the most logical first step should be to involve people with lived experience of mental distress in the redevelopment of the DSM. Accounts from people with lived experience of mental distress are directly relevant to the design of the DSM, as they provide a more comprehensive and accurate understanding of mental distress and its treatment ( 96 ). Moreover, the DSM’s conceptualization as a major determinative classification system could be standing at the core of psychiatry’s “identity crisis”, where checklists of symptoms replaced thoughtful diagnoses despite after decades of brain research, no biomarker has been established for any disorders defined in the DSM ( 10 , 97 ).

Design approaches can help DSM task forces prioritize integrating lived experiences to co-create a framework that can accommodate a range of perspectives to make it viable as a conversation piece. As DSM classifications do not reflect reality ( 98 ), listening to people with firsthand experiences is necessary. The CHIME framework – a conceptual framework of people’s experiences of recovery – shows, for example, a clear need to diagnose not solely based on symptoms but also considering people’s stages in their journey of personal recovery ( 80 ). Further, bottom-up research shows that the lived experience perspective of psychosis can seem very different compared to conventional psychiatric conceptualizations ( 82 ). This is also the case for the lived experience of depression ( 99 ). Design approaches can ensure that such much-needed perspectives and voices are adhered to in developing meaningful innovations ( 88 ), which brings us back to the design of the DSM. Although the DSM aims to conceptualize the reality of mental distress, engaging people with experiences of living with mental distress has never been prioritized by the DSM task force as an important epistemic resource. This is evidenced by the historically low engagement of people with lived experiences and their contexts. For example, although “individuals with mental disorders and families of individuals with mental disorders” participated in providing feedback in the DSM-5 revisions process ( 14 ), when and how they were involved, what feedback they gave, and how this was incorporated are not described. According to the Involvement Matrix ( 100 ) — a matrix that can be used to assess the contribution of patients in research and design —, giving feedback can be classified as ‘listeners’ or ‘co-thinkers,’ which are both low-involvement roles. Moreover, a review of the members of the DSM task forces and working groups listed in the introductions of the DSMs shows patients have never been part of the DSM task force and thus never been part of the decision-making process ( 96 ). Human-centered design is difficult to achieve when people with lived experience are not involved from preparation to implementation but are only asked to give feedback on expert consensus ( 88 ).

In the participation era, using a design approach in mental health care without engaging important stakeholders can be problematic. For example, it is evident that the involvement of people with lived experience changes the nature of an intervention dramatically, as people’s unique first-hand experiences, insights about mental states, and individual meaning and needs are often different in design activities as opposed to what general scientific and web-related resources suggest ( 101 , 102 ). Further, clear differences are reported around designers, researchers, and clinicians on one side and service user ideas of meaningful interventions on the other ( 102 , 103 ). Thus, the meaningful engagement of people with lived experience in design processes always exposes gaps between general research and the interests and lives of service users ( 104 ). This makes the participation of people with lived experience in developing innovative concepts — and, as such, in the conceptual design of DSM spectra of mental distress — essential because their absence in design processes may lead to ineffective outcomes ( 102 ). This design perspective may explain some of the negative effects of the DSM. The classifications aimed to be empirical constructs reflecting reality, yet phenomena such as reification and the classificatory looping effect emerged ( 42 , 51 ). From a design perspective, the emergence of these effects may have a simpler explanation than previously presumed: the premature over-commitment in the DSM’s design processes without input from individuals with firsthand experiences.

3.3 Shifting the premature over-commitment to iterative exploration

The centrist development approach used to design the DSM implicitly frames people with mental distress as ‘ordinary people,’ resulting in ‘average solutions’ because their experiences are decontextualized and lumped together on a group level — eventually leading to general descriptions for a universal appliance. Instead, a more human-centered iterative design process in which people with lived experience play an important role, preferably as decision-makers, can promote the design of spectra of mental distress that leave room for idiosyncrasies that correspond with people’s living environments on an individual level. This can potentially ensure that they are actually helpful for shared decision-making between patients and professionals and resonate in person-centered mental health care. A design approach is feasible for this aim because design processes are not searching for a singular ‘truth’ but rather exploring the multiple ‘truths’ that may be relevant in different contexts ( 105 ). This can be of added value to conceptualizing spectra of mental distress, which is known to have characteristics that overlap between people but also to have a unique phenomenology and contextual foundation for each individual — in the case of mental distress, there literally are multiple truths dependable on who and what you ask in what time and place. Furthermore, design approaches enable exploration and discovery ( 106 ). Designers consistently draw cues from the environment and introduce new variables into the same environment to eventually discover what does and does not work ( 107 ). This explorative attitude also ensures the discovery of unique insights, such as people’s experiential knowledge and contexts. Therefore, from a design perspective, predetermining solutions might be ineffective for arriving at DSM innovation. This is, for example, aptly described by Owens et al. ( 101 ):

“… the iterative nature of the participatory process meant that, although a preliminary programme for the whole workshop series was drawn up at the outset, plans had to be revised in response to the findings from each session. The whole process required flexibility, a constantly open mind and a willingness to embrace the unexpected”.

These insights illustrate the core of design that can guide the development of future DSM iterations: design enables the task force to learn about mental health problems without an omniscient perspective by iteratively developing and testing conceptualizations in the environment in partnership with the target group. As participatory design studies consistently demonstrate, solutions cannot be predetermined solely based on research and resources. The involvement of individuals with lived experience and their contexts invariably uncovers crucial serendipitous insights that challenge the perspectives on the problem. This can expose important misconceptions, such as the tendency to underestimate the complexity of human experience and decontextualize it from its environment.

3.4 Insights that could inform a procedure for co-designing spectra of mental distress

People with lived experience need to be highly involved in developing meaningful spectra of mental distress to guide conversations in clinical practice. As we now have a comprehensive understanding of what design approaches can offer to the development procedure of a lived experience-informed DSM, we will highlight these insights in this paragraph.

3.4.1 Balance academic research with lived experience insights

In the design procedure of a future DSM, academic research can be used to learn about people’s experiences of mental distress but never as the source alone for the development of spectra of mental distress. In this way, designers and researchers in mental health care need to involve people with lived experience at the heart of design processes as partners and come to unique insights together without an omniscient perspective. The aim should not be to design general descriptions but to design spectra that are flexible enough to adapt to local needs and constraints for the various parties using them yet robust enough to maintain a common identity across different locations. This allows the DSM to have different meanings in different social worlds, while at the same time, their structure is common enough for more than one world to recognize them.

3.4.2 Prevent premature overcommitment in the design process

Conceptualizations of spectra of mental distress must not be predetermined, and there should be no overcommitment to concepts in the early phases of the project. Thus, the task force should avoid viewing mental distress too narrowly, too early on in the process. This enables the evolution of lived experience-based spectra in an iterative design- and test process. The starting point should be an open representation of mental distress and discover together with people with lived experience how this could be best conceptualized and what language should be used. This allows room for exploring and discovering what works and aligns with patients’ needs and experiences in their living environments and professionals’ needs in their work environments.

3.4.3 Designing and testing is also a form of research

Researchers and designers should realize that designing and testing conceptualizations in partnership with people with lived experience also results in unique knowledge that can guide the development — designing and testing the developed concepts is a form of research. For example, exploring if a certain designed spectrum resonates as a conversation piece between patients and professionals in clinical practice provides qualitative insights that cannot be predicted beforehand. In this way, science and design can complement the innovation of the DSM: science benefits from a design approach, while design benefits from scientific methods ( 108 ). Flexible navigation between design and science would indicate that the developed DSM can be meaningful as a conversation piece in clinical practice.

3.4.4 Good design comes before effective science

Good design comes before effective science, as innovations are useless if not used, even if they are validated by science ( 85 ). Although the development of the DSM is often described as a scientific process, our analysis indicates that it is more accurately described as a design process. As a design process, it requires a methodologically sound design approach that is suitable for involving patients and people with lived experience. Co-design is a great contender for this purpose, as a systematic review showed this approach had the highest level of participant involvement in mental health care innovation ( 89 ). Although people with lived experience have never been involved as decision-makers, this should be the aim of the design process of a novel DSM in the participation era. This promotes lived experience leadership in design and, ultimately, contributes to more effective science.

3.4.5 Avoid tokenism and co-optation

Involving people with lived experience as decision-makers in redesigning the DSM must avoid tokenism and co-optation and address power imbalances. The first step that the task force can take is to use the Involvement Matrix ( 100 ) together with people with lived experiences to systematically and transparently plan, reflect, and report on everyone’s contribution to the design process. This has not been prioritized in the past DSM revisions. In the end, transparency and honesty about collaboration can support the empowerment of people with firsthand perspectives and shift the power imbalance towards co-creation for more human-centered mental health care. This is needed, as the involvement of people with lived experience in design and research processes is currently too low and obscured by vague terms and bad reporting.

4 Discussion and conclusion

In this hypothesis and theory paper, we have argued that the current role of the DSM, as an operating manual for professionals, can be reconsidered as a boundary object and conversation piece for patients and professionals in clinical practice that stimulates dialogue about mental distress. In this discussion, we will address five themes. First, while we argued that research acknowledges the absence of empirical support for biological causation, we believe characterizing the DSM as entirely non-empirical may be incorrect. Second, we discuss our perspective on balancing between a too-narrow medical perspective and a too-broadly individualized perspective. Third, we discuss why mental health care also needs novel methods for inquiry if the DSM is reconsidered as a conversation piece. Fourth, we discuss that while we are certain that design approaches can be fruitful for redesigning the DSM, some challenges regarding tokenism, co-optation must be addressed. We conclude by examining various methodological challenges and offering recommendations for the co-design process of the DSM.

4.1 Redesigning instead of discarding the DSM

The DSM is too deeply entrenched in mental health care to discard it simply. The DSM is embedded in not only mental health care but also society. For instance, a DSM classification is necessary in the Netherlands to get mental health care reimbursement, qualify for additional education test time, or receive subsidized assisted living. Moreover, it is ingrained in research and healthcare funding, making it unproductive and somewhat dangerous to discard without an alternative, as it may jeopardize access to care and impact insurance coverage for treatment and services that people with mental distress need. Therefore, we posited that instead of discarding the DSM, its role should be reconsidered in a mental health care system centered around shared decision-making and co-creation to eliminate pervasive effects such as the disengagement of patients, reification, disorderism, and the psychiatrization of society. However, the DSM categories are not entirely a priori constructed as is sometimes claimed, as the psychiatric symptom space and diagnostic categories took shape in the late nineteenth century through decades of observation ( 109 ).

While this adds important nuance to the idea that the design of the DSM is entirely non-empirical, it does not invalidate the argument that the DSM design is grounded in a potentially false ontology ( 64 ). Though the lack of evidence does not necessarily indicate evidence of absence, and the biological context in some way plays a role, research shows various other dimensions of life — including the social, historical, relational, environmental, and more — also influence mental distress, yet are significantly underemphasized in its current design. We believe that we showed this manifests itself most prominently in the various highly arbitrary classification designs that can confuse the professional and the patient and appear limited in providing meaningful guidance for clinical practice, design, and research. That is why we have proposed redesigning the next iteration of the DSM to primarily focus on formulating a set of spectra of distress. Reconsidering the DSM leverages one of its biggest strengths: the DSM is not bound by an analytic procedure but rather is guided by scientific debate ( 17 ). Further, developments and amendments to psychiatric classification systems have always reflected wider social and cultural developments ( 110 ). The recognition, implementation, and impact of the DSM in Western countries can even be seen as a reason not to focus on developing alternative models but rather to redesign the DSM so that it conceptually aligns with the social developments, scientific findings, and needs of people in the 21st century, as it is already deeply embedded in systems. Given that DSM classifications are now recognized as inaccurate depictions of the reality of mental distress ( 98 ) and that, at the same time, mental health care is shifting towards person-centeredness and shared decision-making, we believe the proposals in this article are not radical but rather the most meaningful way forward to accommodate diverse perspectives.

4.2 Balancing between a too-narrow medical categorization and a too-broadly individualized approach

From a classical psychopathological perspective, integrating the lived experiences of those with mental distress into the redevelopment of the DSM as a boundary object presents certain conceptual challenges. For example, uncritically overemphasizing individual experiences might lead to an underappreciation of psychopathological manifestations like, for example, altered perceptions. Conversely, excluding people with lived experience from the DSM’s design processes has resulted in its own conceptual and epistemic issues, such as undervaluing the idiographic, contextual, and phenomenological aspects of individual mental distress. Therefore, we argue that achieving a balance between these differing but crucial perspectives should result from a co-design procedure for a revised DSM. Determining this balance before obtaining results from such a process is too premature and arbitrary and would contradict our recommendation to prevent over-commitment in the early stages of the design process. As people with lived experience were never previously involved, it is impossible to predict the outcomes of a co-design procedure or hypothesize about a clear distinction between these perspectives in the DSM’s conceptual development beforehand. As seen in past iterations, prematurely drawing rigid lines could hinder the design process and result in design fixation. From the perspective of boundary objects, the DSM cannot have one dominant perspective if it is to function effectively. All stakeholders must be able to give meaning to the spectra of mental distress from their own activity systems, and these perspectives should be equal in order to create a shared awareness of the different perspectives involved. A DSM designed as a boundary object triggers dialogical learning mechanisms, ensuring the multiple perspectives are harmonized rather than adjusted to fit one another, ensuring no single perspective prevails over the others or consensus is pursued ( 71 , 111 ).

4.3 Novel methods for inquiry to accompany the reconsidered role of the DSM

If the DSM is reconsidered and designed as a conversation piece and classifications are replaced by spectra, in clinical practice, a unique language needs to be co-developed between the patient and the professional, and an equal relationship is important to ally. For example, if we consider the person-specific meanings of altered perceptions, they need to be explored, as they have clinical relevance. However, for such purposes, current diagnostic methods in clinical practice are limiting because they are highly linguistic and tailored to classification systems and the needs and praxis of the professionals. This can impede the DSM’s effectiveness as a tool for dialogue. Expressing the uniqueness of an experience of mental distress is difficult — especially during a mental crisis — let alone effectively communicating it to a professional. While people with mental distress can effectively communicate their behaviors and complaints, which fits the current use of the DSM, people have far more embodied and experiential knowledge of their distress. How people cope with their mental distress in the contexts they are living in is very difficult to put into words without first making these personal and contextual insights tangible ( 41 ), yet this is essential information for when the DSM is used as a boundary object and conversation piece. To accommodate the patient in making this knowledge tangible, the professional becomes more of a facilitator than an expert, emphasizing therapeutic relationships and the healing effects of ritualized care interactions ( 39 ). This transformation requires novel co-creative methods for inquiry ( 41 ) and professional training ( 39 ). Therefore, expanding the diagnostic toolkit with innovative and creative tools and embracing professionals such as art therapists, social workers, and advanced nurse practitioners to enable and support patients to convey their narratives and needs in their own way is essential if the DSM is to be used as a boundary object and conversation piece.

4.4 Promoting lived experience leadership in the co-design procedure

Despite longstanding calls for the APA to include people with lived experience in the decision-making processes for diagnostic criteria, the DSM-5 task force did not accept this inclusion. The task force believed incorporating these perspectives could compromise objectivity in the scientific process ( 96 ). This mindset ensures that research, design, and practice remain predominantly shaped by academics and professionals, causing conventional mental health care to perpetuate itself. It continues to repeat the same approaches and consequently achieves the same results. Therefore, people with lived experience should have more influence in the participation era to accelerate change in mental health care. This proposition comes with some challenges regarding power imbalances that need addressing. While it is acknowledged that the involvement of individuals with lived experience yields unique insights and can serve as strong collaborators and knowledgeable contributors, they are never given decision-making authority in design processes in mental health care ( 88 , 89 , 92 ) or in the DSM’s development processes ( 96 ). This lack of authority impedes lived experience leadership ( 91 , 112 ) and subsequently stands in the way of effectively reconsidering and redesigning the DSM. To avoid tokenism, the DSM revision process should not settle for low engagement and involvement but set the bar higher by redressing power imbalances ( 113 ). Furthermore, in the co-design process of the DSM, the task force should not view objectivity as the opposite of subjectivity or strive for consensus. Instead, they should value group discussions and disagreements, encouraging stakeholders to debate and explore the sources of their differing perspectives and knowledge ( 96 ). Shifting towards lived experience leadership starts with perceiving and engaging people with lived experiences of mental distress as experts of their experiences in iterative design and research processes and giving them this role in revising the DSM.

4.5 Methodological considerations for a co-design procedure of the DSM

Merely positioning people with lived experience as partners and decision-makers is insufficient; there are also significant methodological concerns regarding the execution of design research in mental health care. Although iteration and participation are essential for design in mental health care, as designers focus on the unmet needs of service users and ways to improve care ( 114 ), research shows design is not always executed iteratively, and end users are not always involved. For example, about one-third of projects that designed mental health interventions did not adopt an iterative process ( 85 ). The engagement of end users in design processes in mental health is also not yet a common practice. For instance, a systematic review of serious games in mental health for anxiety and depression found that only half of these games, even while reporting using a participatory approach, were designed with input from the intended end-users ( 115 ). A systematic review of design processes that aimed to design innovations for people with psychotic symptoms overlaps these findings, as less than half of the studies demonstrated a high level of participant involvement in their design processes ( 89 ).

The low level of involvement and lack of iterative approaches in mental health care design offer valuable insights for future processes. If the DSM task force aims to adopt a co-design approach, it should incorporate these lessons to enhance design effectiveness. First, the task force must understand that design has a different aim, culture, and methods than the sciences ( 116 ). The scientific approach typically implies investigating the natural world through controlled experiments, classifications, and analysis, emphasizing objectivity, rationality, neutrality, and a commitment to truth. In contrast, a design approach focuses on studying the artificial world, employing methods such as modeling, pattern formation, and synthesis, guided by core values of practicality, ingenuity, empathy, and concern for appropriateness. Second, the task force should consider the known challenges they will encounter and need to navigate to let the paradigms be complementary in practice ( 117 ). Further, the task force should consider that the nature of design is exploratory, iterative, uncertain, and a social form of inquiry and synthesis that is never perfect and never quite finished ( 84 ). This requires tolerating ambiguity and having trust ( 101 ). Lastly, more transparency in the participatory work of the task force is called for, beginning with being honest, being detailed, addressing power imbalances, being participatory in reporting the participatory approach, and being excited and enthusiastic about going beyond tokenistic engagement ( 118 ).

Despite these challenges, transforming psychiatric diagnoses by reconsidering and redesigning the DSM as a boundary object and conversation piece could be a step in the right direction. This would shift the power balance towards shared ownership in a participation era that fosters dialogue instead of diagnosis. We hope this hypothesis and theory paper can give decisive impulses to the much-needed debate on and development of psychiatric diagnoses and, in the end, contribute to lived experience-informed psychiatric epistemology. Furthermore, as a product of an equal co-production process between various disciplines and types of knowledge, this paper shows it is possible to harmonize perspectives on a controversial topic such as the DSM.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author/s.

Author contributions

LV: Conceptualization, Methodology, Project administration, Writing – original draft, Writing – review & editing. GT: Conceptualization, Methodology, Visualization, Writing – original draft, Writing – review & editing. JVO: Conceptualization, Writing – original draft, Writing – review & editing. SM: Conceptualization, Writing – original draft, Writing – review & editing. JV: Writing – original draft, Writing – review & editing. NB: Writing – original draft, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. We appreciate the financial support of the FAITH Research Consortium, GGZ-VS University of Applied Science, as well as from the NHL Stenden University of Applied Sciences PhD program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Acknowledgments

We thank the reviewers for their thorough reading of our manuscript and valuable comments, which improved the quality of our hypothesis and theory paper.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: psychiatry, diagnosis, design, innovation, mental health care

Citation: Veldmeijer L, Terlouw G, van Os J, te Meerman S, van ‘t Veer J and Boonstra N (2024) From diagnosis to dialogue – reconsidering the DSM as a conversation piece in mental health care: a hypothesis and theory. Front. Psychiatry 15:1426475. doi: 10.3389/fpsyt.2024.1426475

Received: 01 May 2024; Accepted: 22 July 2024; Published: 06 August 2024.

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*Correspondence: Lars Veldmeijer, [email protected]

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• Published: 10 August 2024

A new look at the CO 2 haven hypothesis using gravity model European Union and China

• Somayeh Avazdahandeh 1  

Scientific Reports volume  14 , Article number:  18610 ( 2024 ) Cite this article

Metrics details

• Environmental economics
• Environmental sciences
• Environmental social sciences

The pollution haven hypothesis (PHH) is defined as follows: a reduction in trade costs results in production of pollution-intensive goods shifting towards countries with easier environmental laws. The previous studies examined this hypothesis in the form of Kuznets' environmental hypothesis. In this way, they test the effect of foreign direct investment (FDI) on carbon emissions. However, this study investigates PHH from a new perspective. I will use Newton's gravity model to test this hypothesis. The basis of PHH is the difference in the environmental standards of the two business partners. One of the indicators used to measure the severity of a country's environmental laws is carbon emission intensity. The stricter the country's laws are, the lower the index value will be. In order to test the hypothesis, experimental data from China and OECD countries are used. China was as the pollution haven for the countries of the Organization for Economic Cooperation and Development. I found that environmental laws of host and guest countries have different effects on FDI. In addition, transportation costs have a negative effect on the FDI flow. Finally, the research results confirm the hypothesis on gravity model.

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Introduction.

The discussion on link between environment and trade started in 1970’s. This debate become serious in 1990’s when trade openness was expanded by different organizations like North American Free Trade Agreement (NAFTA). Copeland and Taylor 1 first introduced the PHH in the context of North–South trade under NAFTA. It was the first article that links the environmental rules severity and trade models with the level of pollution in a country 2 . They proved in the first and second propositions that the higher income country chooses harder environmental safekeeping, and specializes in relatively clean commodities 1 . These two propositions are actually the pollution haven hypothesis. As stated by the PHH, the movement of the unclean industries from advanced to developing countries happen by way of the trade of commodities and foreign direct investment (FDI) 2 . Two important factors are the basis of the pollution haven hypothesis. The first is foreign investment, and the second is environmental laws. The first factor, foreign direct investment (FDI) is an impartible item of an open and effective international economic process and an important catalyst to development 3 . FDI flow is an increase in the book value of the net worth of investments in one country held by investors of another country, where the investments are under the managerial control of the investors 4 . Developing and emerging economies have come passing through increasingly to see FDI as a factor of economic development and advance, income growth and employment 3 . The mutual connection between trade and FDI is an important feature of globalization. Empirical study shows that, until the mid-1980s, international trade generated direct investment. After this era, the cause-and-effect relationship has converted and direct investment has a huge impact on international trade 3 . In the today's economies, trade has an increasingly important effect in shaping economic and social performance and prospects of countries around the world, especially those of developing countries 5 .

The second factor was environmental laws (EL). Generally, international trade has two consequences on the environment. First, trade can improve environmental quality by exporting clean technologies from developed countries to developing countries. In fact, trade can improve environmental quality 6 , 7 . Second, international relations can increase pollution. More developed countries pay more attention to environmental standards. In these countries, with the increase in economic growth, people demand a higher quality of the environment. It is the opposite in less developed countries. They substitute higher economic growth for environmental quality. According to the Kuznets environmental curve, developed countries are located after the turning point, while less developed countries are located before this point. In other words, more developed countries have stricter environmental laws than less developed countries. If we want to have a definition of environmental laws: environmental rules is a series of policies or standards adopted by the government to maintain the environment 8 . Environmental regulations have effectively restricted the damages of enterprises by the environment and had an important role in protecting the environment 8 , 9 .

Paying more or less attention to environmental standards reminds us of the pollution haven hypothesis. The pollution haven hypothesis, which emerged in the 1990s, pivots on the relocation of polluting manufacturing from developed countries with hard environmental laws to developing countries with soft rules 10 . The pollution haven hypothesis tells that easy environmental rules in developing countries encourage investment in emission-intensive industries from developed countries, especially in the context of increasing numbers of countries committing to carbon neutrality before 2050 11 .

From the PHH standpoint, the stringent environmental rules in developed countries lead to relocate of the polluting industries from developed to developing countries and cause pollution to rise in developing countries 2 . According to the definition of PHP, what causes the movement of FDI between two countries is the severity of environmental laws. In other words, their environmental regulations determine the amount of FDI of two countries. As we mentioned earlier, PHP is Copeland and Talor 1 first and second presentation in their article. Therefore, the purpose of this article is to model the pollution haven hypothesis accurately. By carefully investigating this hypothesis and previous studies (Table 1 ), we found that there are two important gaps: first, none of them has included the two main factors of the hypothesis (FDI and EL) in their modeling simultaneously. Second, they assumed that FDI has an effect on EL. That is, they considered the FDI variable as independent. While FDI should be considered as dependent variable. Therefore, this research will make future researchers have a more accurate view of PHP. They can examine the effects of various social, economic, environmental and political variables in this new model. The framework presented by this paper allows researchers to better understand the distinction between the two environmental Kuznets and the pollution haven hypotheses.

So far, many researchers have investigated the pollution haven hypothesis. In these studies, different indicators have been used for the first factor of PHH. Some of them directly applied FDI, for example Usama and Tang 12 ; Solarin et al. 13 ; Benzerrouk et al. 7 ; Shijie et al. 14 ; Temurlenk and Lögün 15 ; Yilanci et al. 16 ; Ali Nagvi et al. 17 ; Chirilus and Costea 18 ; Campos‑Romero et al. 19 ; Liu et al. 20 ; Soto and Edeh 21 ; Ozcelik et al. 22 . Some researchers also used polluting goods and activities as proxies, here are some of them: Shen et al. 23 ; Sadik-Zada and Ferrari 24 ; Zhang and Wang 25 ; Bhat and Tantr 10 ; Moise 26 ; Hamaguchi 27 . In all studies, the first PHP factor is considered as an independent variable. These studies are briefly mentioned in Table 1 .

By reviewing previous research, the innovation of this study is the methodology section. The research methodology is based on Newton's gravity model. I will present the pollution haven hypothesis in terms of Newton's gravity model. This model is widely used in trade research. There are always two partners in business discussions. One country will be the importer and the other the exporter. The pollution haven hypothesis is also one of the business categories. The importer will become a pollution haven for the exporting country. In fact, the host country will become a trading partner's haven for the investment of polluting industries.

I selected the host country based on the pollution emission and foreign direct investment data in 2020 (Figs. 1 , 2 ).

( a ) Ten countries with the most CO 2 (kt) emissions in 2020, ( b ) ten countries with the most share of CO 2 emissions (percentage) in 2020.

( a ) Ten countries with the highest foreign direct investment (FDI) net inflows in 2020, ( b ) ten countries with the highest share of foreign direct investment (FDI) inflows in 2020.

Countries have different levels of CO 2 emissions based on their activities. As Fig.  1 a shows, China had the highest CO 2 emissions in 2020 (about 13 million kilotons). On the other hand, China's share of the world's total emission is more than 29% (Fig.  1 b). United States is next with a share of 12%. According to Fig.  1 b, China's share is two and a half times that of United States.

Foreign direct investment is an important indicator to determine the pollution haven. We reviewed the FDI countries of the world in 2020. The results of the investigation are shown in Fig.  2 a,b. Figure  2 a indicates that China has the highest FDI inflows in 2020. China had 2.5 thousand billion FDI net inflows in 2020 (Billions of United States dollars). According to Fig.  2 b, China's FDI share in 2020 was 21%.

After introduction, the results section is discussed in detail. The research findings were divided into three categories: 1. the validity of the pollution haven hypothesis. 2. The effect of control variables on foreign direct investment. 3. The effect of main or independent variables on foreign direct investment. Generally, results showed that the severity of environmental laws has different effects on FDI flow. Since the direction of FDI flow from OECD countries is to China, increasing the severity of the environmental regulations of the guest countries will increase FDI. On the other hand, more environmental laws of China (the host) reduce the flow of FDI.

Results and discussion

This section presents and discusses the main findings of the empirical analysis. In this research, I investigated pollution haven hypothesis based on gravity model approach. The variables that were collected included CO 2 emission (World Bank), GDP (World Bank), trade costs (World Trade Organization), FDI inflows from OECD to China (Organization for Economic Co-operation and Development data), urbanization is represented by the number of individuals living in cities (World Bank), trade openness, whose calculation formula is as: X + M/GDP, where X = Exports; M = Imports) (World Bank) and share of manufacturing (World Bank). Table 2 indicates the measurement unit of the variables and their sources.

Because data has two dimensions (cross-section and period), I used the F-Limer test to determine whether the data is a panel. The null hypothesis was rejected based on the pool model and the model with panel data was accepted, Therefore, I used panel regression. Then, Hausman test was used to test the type effects (random or fixed). The result showed that there was a random effect for both cross-section and period. In the following, I investigated stationary of the variables to prevent spurious regression. Levin, Lin and Chu test, examined four variables. The result showed that variables are stationary at level (intercept and trend).

Finally, I estimated the model based on panel data. The regression results has been shown in Table 3 . FDI ijt is the dependent variable. ER it , ER jt , TC 2 ijt , \({\text{lnUR}}_{\text{jt}}\) , \({\text{lnTO}}_{\text{jt}}\) and \({\text{lnShM}}_{\text{jt}}\) are independent variables. Table 3 indicates that all the coefficients are significant at the level of 5% and R 2 was 0.79. R 2 shows that the independent variables were able to explain 79% of the changes in the dependent variable.

The coefficient for \({lnER}_{it}\) is − 0.54. The negative coefficient sign shows that with increasing \({ER}_{it}\) , FDI ijt will decrease. In other words, when host countries' environmental regulations become easier, FDI flows from host countries (OECD) to host countries (China) will decrease. The coefficient for \({lnER}_{jt}\) is 0.90. The positive sign indicates that if \({ER}_{jt}\) increases, FDI ijt also increase. The results related to the effect of environmental laws on trade were similar to the previous studies. For example, in Shen et al. 23 study, the coefficient sign for environmental regulation was positive. Sadik-Zada and Ferrari 24 indicated that environmental policy stringency has a positive effect on carbon trade. Bhat and Tantr 10 concluded that environmental policy has a positive effect on pollution-intensive exports. The coefficient for transportation costs was obtained − 0.11. In fact, with the increase in transportation costs, the FDI flow from i (guest country) to j (host country) decreases. The \({\text{lnTC}}_{\text{ijt}}^{2}\) coefficient sign in present study is consistent with the following studies: Nuroğlu and Kunst 28 ; Wang et al. 29 ; Golovko and Sahin 30 ; Wani and Yasmin 31 . Among the control variables, the urbanization coefficient was not significant. The coefficient for the TO and ShM was positive and significant. The coefficient for the TO was positive. It means that as TO increases, FDI also increases. Benzerrouk et al. 7 indicated that an increase in trade and FDI increases the developed countries’ polluting projects which are destined for the developing countries. Moise 26 showed that trade openness statistically and significantly increase CO2 emission. In addition, the coefficient value for the ShM was estimated positive. This coefficient states that if the share of manufacturing in GNP increases, foreign direct investment will increase. Shijie et al. 14 concluded the positive effect of FDI on the environment of dominant industrial agglomeration is increasing first and then decreasing. Sawhney and Rastogi 32 indicated that that the increase in trade liberalization, the growth of American industries and FDI has increased the emission of pollution in India.

Our model is in terms of logarithms, so the coefficients express elasticities. \({LnER}_{it}\) and \({ln ER}_{jt}\) coefficient were − 0.54 and 0.9, respectively. That is, if the environmental laws of guest country are tightened by 1%, FDI flow from the guest country to the host country will decrease by 54%. In addition, if environmental laws of the host country are relaxed by 1%, FDI flow from the guest country to the host country will increase by 90%. The \({lnTC}_{ijt}^{2}\) coefficient was − 0.11 that indicates with 1% increase in transportation costs, the FDI flow from the guest country to the host country decreases by 11%. The coefficient for the control variables \({lnTO}_{jt}\) and \({lnShM}_{jt}\) were 0.77 and 0.51, respectively. These coefficients state that if trade openness and share of manufacturing increase by 1%, foreign direct investment will increase by 71 and 55%. Thus, increasing trade openness reveals that lax environmental enforcement in developing countries attracts investment in emission-intensive industries from developed countries.

This paper fills a research gap by assessing pollution haven hypothesis based on its initial assumptions. In previous studies, this hypothesis was tested in the form of Kuznets curve. While the concept of pollution haven is due to differences in environmental laws. While the concept of pollution haven is foreign investment flow between the haven seeker and the haven giver. The main driver of which is the difference in environmental laws. When we talk about flow, the gravity model is the best option, for example: power flow, trade flow, labor flow, FDI flow (see Fig.  4 in the method section). Trade has permitted countries with more emission intensities to export goods or investment to countries with less emission intensities, which may result an increase in worldwide carbon emissions 11 . In this research, we examined foreign direct investment between OECD countries and China. In fact, we investigated the effect of environmental laws on FDI in the form of the pollution haven hypothesis. The indicator chosen for the environmental regulations was emission intensity. Figure  3 shows carbon emission intensity of guest (OECD) and host (China) countries in 2016–2020 (kt/10 billion $). Figure  3 (1–9) is for OECD countries and Fig.  3 (10) is for China. As Fig.  3 indicates, emission intensity has been decreasing in all selected countries in 2016–2020.

Carbon emission intensity of guest (OECD) and host (China) countries in 2016–2020. (1–9) is for OECD countries. (10) is also for China. Carbon emission intensity in kilotons per 10 billion dollars.

The emission intensity is in range (782–5439) for OECD countries, while it is in (5510–6308) kilotons per 10 billion dollars for China. The maximum emission intensity of OECD countries is lower than the minimum emission intensity of China. It means that the environmental rules of guest countries are stricter than China. Therefore, the pollution haven hypothesis discloses that weak environmental implement in developing countries absorbs investment in emission-intensive industries from developed countries 11 . The purpose of this paper is to model the pollution haven hypothesis in the form of gravity model. For this purpose, the effect of environmental laws of host and guest countries on FDI is investigated. The results showed that the severity of environmental laws has different effects on FDI flow. Since the direction of FDI flow from OECD countries is to China, increasing the severity of the environmental regulations of the guest countries will increase FDI. On the other hand, more environmental laws of China (the host) reduce the flow of FDI. After presenting the results and comparing them with previous studies, the results should be tested for robustness. The main empirical findings are robust to two different methods of multicollinearity tests (i) Cross-correlation across variables (ii) Variance inflation factor (VIF) of each variable 33 . VIF is a measure of the amount of multicollinearity in regression model. Multicollinearity exists when there is a correlation between multiple independent variables.

The variance inflation factor is calculated as follows:

where \({R}_{i}^{2}\) is the variance explained by the regression model (i is counter of explanatory variable). On the other hand, \({R}_{i}^{2}\) represents the regression of the predictor of interest on the remaining predictors. The VIF values cannot be less than 1.0, since 1.0 represents the ideal situation of no correlation with other predictors. Also implied is that VIF cannot be negative. The minimum VIF can be is 1.0. A VIF of 1.0 can only occur when \({R}_{i}^{2}\) is equal to 0, which implies that the given predictor has zero linear relationship with other predictors in the model. Tolerance is simply the reciprocal of VIF and is thus computed as

whereas large values of VIF are undesirable, large tolerances are preferable to smaller ones. It stands as well that the maximum value of tolerance must be 1.0 34 . As shown in Table 4 , the mean variance inflation factor (VIF) values in our model is equal to 2.34. The maximum VIF amount of explanatory variables is 4.62, which lie within the acceptable standard.

In this study, the pollution haven hypothesis was investigated from a new perspective. In fact, this hypothesis was formulated based on theoretical foundations. Copeland and Taylor 1 first introduced the PHH. It was the first article that links the environmental rules severity and trade models with the level of pollution in a country. According to Copeland and Taylor 1 's article, two factors of foreign direct investment and environmental laws constitute this hypothesis. In their study, it is stated that the environmental laws of countries determine the attraction of foreign direct investment. Therefore, we considered FDI between two countries as a function of their environmental laws. To achieve the research objectives, the commercial gravity model was used. The research innovation is first, none of authors has included the two main factors of the hypothesis (FDI and EL) in their modeling simultaneously. Second, they assumed that FDI has an effect on EL. That is, they considered the FDI variable as independent. While FDI should be considered as dependent variable. Therefore, this research will make future researchers have a more accurate view of PHP. The results showed that if environmental laws of guest country are tightened by 1%, FDI flow from the guest country to the host country will decrease by 54%. In addition, if environmental laws of the host country are relaxed by 1%, FDI flow from the guest country to the host country will increase by 90%. The \({lnTC}_{ijt}^{2}\) coefficient was − 0.11 that indicates with 1% increase in transportation costs, the FDI flow from the guest country to the host country decreases by 11%. The coefficient for the control variables \({lnTO}_{jt}\) and \({lnShM}_{jt}\) were 0.77 and 0.51, respectively. These coefficients state that if trade openness and share of manufacturing increase by 1%, foreign direct investment will increase by 71 and 55%. After presenting the results and comparing them with previous studies, the results should be tested for robustness. The mean VIF values in our model is equal to 2.34. The maximum VIF amount of explanatory variables is 4.62, which lie within the acceptable standard.

Based on the results of the research, suggestions are provided. But rigidity in environmental law could lead to reduce in FDI. On the other hand, FDI is a key factor for economic growth and development; hence, FDI can be transferred from polluting sectors to clean sectors, such as service sectors, labor-intensive industries or renewable energy sectors, and green technology investment, should be encouraged. The manufacturing sector is the largest contributor to global emissions when direct and indirect emissions are included. The key transformations needed to bring the industry sector towards environmentally friendly goals. These aims can include electrifying industry, transform production processes, using new fuels, accelerating material efficiency and scaling up energy efficiency everywhere, and promote circular material flow. Openness trade, like industry growth, increases FDI. Furthermore, to decrease the impact of trade openness and economic growth on environmental sustainability, it is very important to increase environmental friendly production system industries that could motivate green technology knowledge for all economic sectors. The receiving countries should improve their mechanism of absorption ability. The paper author has suggestions for future researchers. The regression model that was chosen is a linear model. Therefore, future authors can use other regression methods such as spatial regression and get results that are more accurate. Because one of the variables of the attraction model is the distance between countries. The dependent variable is FDI, which is also affected by key qualitative factors. In future studies, the effect of these qualitative variables can be investigated, such as government structure and investment management factors.

This section contains information about the empirical model. The empirical model is borrowed from common literature on the gravity model. In economic sciences, the gravity model forecasts bilateral trade flows based on measure of the economies (usually using GDP) and distance between the two locations 33 as in Eq. ( 3 ):

In above equation, the gravitational power (G or amount of trade between regions) is positively proportional to the size of the regions ( si and sj ) and negatively proportional to the distance between region (i) and region (j) ( di , j ). In this research, the components of the gravity model are different. In fact, the innovation of this study compared to previous researches is the different components of the gravity model. Figure  4 shows the innovation of this model with previous gravity models.

Newton's gravity model, Trade’s gravity model, FDI’s gravity model.

Two important factors in the pollution haven hypothesis are foreign direct investment between two countries and the severity of the countries' environmental laws. Differences in attention to the environmental quality coupled with trade liberalization may cause to the creation of pollution havens, with polluting activity relocating to areas with weak regulation 35 , 36 . If we consider gravity as FDI. What causes the attraction of foreign direct investment between two countries is the strictness of the countries in implementing environmental regulations. So Eq. ( 3 ) is modified as follows:

where i is country (OECD) i , j is country j (China) and t is time (2016–2020). FDI represents foreign direct investment flow among countries. ER is the severity of environmental laws. Co 2 is pollution emissions. The UR, TO and ShM are urbanization, trade openness and share of manufacturing. The TC denotes the trade costs between countries and GDP is gross domestic product. From Eq. ( 2 ), data is converted into log terms according to traditional methods in econometrics. The equation is established as follows (Eq.  6 ):

Many studies used FDI in their model, such as Usama and Tang 12 ; Solarin et al. 13 ; Benzerrouk et al. 7 ; Temurlenk and Lögün 15 ; Yilanci et al. 16 ; Ali Nagvi et al. 17 . Of course, they considered FDI as an independent variable. But in this study, it is considered as a dependent variable. In Eq. ( 6 ), \(ER\) is the pollution emission intensity, which is calculated in Eq. ( 5 ). In previous studies, several indicators have been used to measure environmental regulations. For example, Guo et al. 37 takes pollutant discharge fee and total investment in pollution controlling to represent environmental rules. Sun et al. 38 applied number of pollution enterprises for environmental rules. Nie et al. 39 make ISO14001 environmental management system certification. Xie et al. 40 used to fail to form fixed assets or form fixed assets for environmental regulation. Sadik-Zada and Ferrari 24 make used environmental policy stringency index as a proxy for PHH.

I measure these rules with the pollution intensity index like Cole and Elliott 41 's study, the proportion of pollution emissions in industrial output value can be used as a proxy for measuring environmental regulations 8 , 41 . In some studies for example Shen et al. 23 ; Bhat and Tantr 10 , emission intensity has been used as the degree of severity environmental regulations. TC also shows transportation costs, which is a proxy for the distance between countries. If we use the distance variable in the model, it would cause collinearity. This study includes annual data of 35 countries from OECD (Fig.  5 ) and China for 2016–2020. The reason for choosing these countries and period is as follows: first, China is the largest emitter of greenhouse gases in recent years. Second, China is the largest importer of foreign investment in recent years. Third, OECD countries were selected because exact information about their FDI inflow to China was available.

Selected countries from among the OECD countries that make foreign direct investment in China.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Somayeh Avazdahandeh

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Avazdahandeh, S. A new look at the CO 2 haven hypothesis using gravity model European Union and China. Sci Rep 14 , 18610 (2024). https://doi.org/10.1038/s41598-024-69611-0

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DOI : https://doi.org/10.1038/s41598-024-69611-0

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