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• 08 June 2021

The COVID lab-leak hypothesis: what scientists do and don’t know

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• Smriti Mallapaty

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Debate over the idea that the SARS-CoV-2 coronavirus emerged from a laboratory has escalated over the past few weeks, coinciding with the annual World Health Assembly, at which the World Health Organization (WHO) and officials from nearly 200 countries discussed the COVID-19 pandemic. After last year’s assembly, the WHO agreed to sponsor the first phase of an investigation into the pandemic’s origins, which took place in China in early 2021 .

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Nature 594 , 313-315 (2021)

doi: https://doi.org/10.1038/d41586-021-01529-3

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Hypothesis Testing | A Step-by-Step Guide with Easy Examples

Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.

There are 5 main steps in hypothesis testing:

• State your research hypothesis as a null hypothesis and alternate hypothesis (H o ) and (H a  or H 1 ).
• Collect data in a way designed to test the hypothesis.
• Perform an appropriate statistical test .
• Decide whether to reject or fail to reject your null hypothesis.
• Present the findings in your results and discussion section.

Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.

Table of contents

Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.

After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.

The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.

• H 0 : Men are, on average, not taller than women. H a : Men are, on average, taller than women.

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For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.

There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).

If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.

Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.

Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .

• an estimate of the difference in average height between the two groups.
• a p -value showing how likely you are to see this difference if the null hypothesis of no difference is true.

Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.

In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.

In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).

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The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .

In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.

In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.

However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.

If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”

These are superficial differences; you can see that they mean the same thing.

You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.

If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

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.

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).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

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."

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 covid-19 lab leak hypothesis: did the media fall victim to a misinformation campaign?

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• The covid-19 lab leak hypothesis: did the media fall victim to a misinformation campaign? - July 12, 2021
• Paul D Thacker , investigative journalist
• thackerpd{at}gmail.com Twitter @thackerpd

The theory that SARS-CoV-2 may have originated in a lab was considered a debunked conspiracy theory, but some experts are revisiting it amid calls for a new, more thorough investigation. Paul Thacker explains the dramatic U turn and the role of contemporary science journalism

For most of 2020, the notion that SARS-CoV-2 may have originated in a lab in Wuhan, China, was treated as a thoroughly debunked conspiracy theory. Only conservative news media sympathetic to President Donald Trump and a few lonely reports dared suggest otherwise. But that all changed in the early months of 2021, and today most outlets across the political spectrum agree: the “lab leak” scenario deserves serious investigation.

Understanding this dramatic U turn on arguably the most important question for preventing a future pandemic, and why it took nearly a year to happen, involves understanding contemporary science journalism.

A conspiracy to label critics as conspiracy theorists

Scientists and reporters contacted by The BMJ say that objective consideration of covid-19’s origins went awry early in the pandemic, as researchers who were funded to study viruses with pandemic potential launched a campaign labelling the lab leak hypothesis as a “conspiracy theory.”

A leader in this campaign has been Peter Daszak, president of EcoHealth Alliance, a non-profit organisation given millions of dollars in grants by the US federal government to research viruses for pandemic preparedness. 1 Over the years EcoHealth Alliance has subcontracted out its federally supported research to various scientists and groups, including around $600 000 (£434 000; €504 000) to the Wuhan Institute of Virology. 1

Shortly after the pandemic began, Daszak effectively silenced debate over the possibility of a lab leak with a February 2020 statement in the Lancet . 2 “We stand together to strongly condemn conspiracy theories suggesting that covid-19 does not have a natural origin,” said the letter, which listed Daszak as one of 27 coauthors. Daszak did not respond to repeated requests for comment from The BMJ .

“It’s become a label you pin on something you don’t agree with,” says Nicholas Wade, a science writer who has worked at Nature , Science , and the New York Times . “It’s ridiculous, because the lab escape scenario invokes an accident, which is the opposite of a conspiracy.”

But the effort to brand serious consideration of a lab leak a “conspiracy theory” only ramped up. Filippa Lentzos, codirector of the Centre for Science and Security Studies at King’s College, London, told the Wall Street Journal , “Some of the scientists in this area very quickly closed ranks.” 3 She added, “There were people that did not talk about this, because they feared for their careers. They feared for their grants.”

Daszak had support. After he wrote an essay for the Guardian in June 2020 attacking the former head of MI6 for saying that the pandemic could have “started as an accident,” Jeremy Farrar, director of the Wellcome Trust and co-signer of the Lancet letter, promoted Daszak’s essay on Twitter, saying that Daszak was “always worth reading.” 4

Daszak’s behind-the-scenes role in orchestrating the statement in the Lancet came to light in November 2020 in emails obtained through freedom of information requests by the watchdog group US Right To Know.

“Please note that this statement will not have EcoHealth Alliance logo on it and will not be identifiable as coming from any one organization or person,” wrote Daszak in a February email, while sending around a draft of the statement for signatories. 5 In another email, Daszak considered removing his name from the statement “so it has some distance from us and therefore doesn’t work in a counterproductive way.” 6

Several of the 27 scientists who signed the letter Daszak circulated did so using other professional affiliations and omitted reporting their ties to EcoHealth Alliance. 3

For Richard Ebright, professor of molecular biology at Rutgers University in New Jersey and a biosafety expert, scientific journals were complicit in helping to shout down any mention of a lab leak. “That means Nature , Science , and the Lancet ,” he says. In recent months he and dozens of academics have signed several open letters rejecting conspiracy theory accusations and calling for an open investigation of the pandemic’s origins. 7 8 9

“It’s very clear at this time that the term ‘conspiracy theory’ is a useful term for defaming an idea you disagree with,” says Ebright, referring to scientists and journalists who have wielded the term. “They have been successful until recently in selling that narrative to many in the media.”

The Lancet ’s editor in chief, Richard Horton, did not respond to repeated requests for comment but, after The BMJ had sent him questions, the Lancet expanded Daszak’s conflicts of interest on the February statement and recused him from working on its task force looking into the pandemic’s origin. 10 11

The Lancet letter ultimately helped to guide almost a year of reporting, as journalists helped to amplify Daszak’s message and to silence scientific and public debate. “We’re in the midst of the social media misinformation age, and these rumours and conspiracy theories have real consequences,” Daszak told Science . 12 Months later in Nature , he again criticised “conspiracies” that the virus could have come from the Wuhan Institute of Virology and complained about “politically motivated organisations” requesting his emails. 13

That summer Scientific American , one of the oldest and best known popular science magazines in America, published a complimentary profile of Daszak’s colleague, Shi Zhengli, a centre director at the Wuhan Institute of Virology, which has been funded by EcoHealth Alliance. 14

EcoHealth Alliance and the Wuhan Institute of Virology earned additional sympathetic reporting after the US National Institutes of Health (NIH) cancelled its grant to EcoHealth Alliance in April last year—allegedly on President Trump’s order—because of its ties to Wuhan, a decision protested by 77 Nobel laureates and 31 scientific societies. 15 (The NIH has subsequently awarded EcoHealth Alliance new funding.)

Efforts to characterise the lab leak scenario as unworthy of serious consideration were far reaching, sometimes affecting reporting that had first appeared well before the covid-19 pandemic. For example, in March 2020 Nature Medicine added an editor’s note (“Scientists believe that an animal is the most likely source of the coronavirus”) to a 2015 paper on the creation of a hybrid version of a SARS virus, co-written by Shi. 16

Wade explains, “Science journalists differ a lot from other journalists in that they are far less sceptical of their sources and they see their main role as simply to explain science to the public.” This, he says, is why they began marching in unison behind Daszak.

By the end of 2020, just a handful of journalists had dared to seriously discuss the possibility of a lab leak. In September, Boston magazine reported on a preprint that found the virus unlikely to have come from the Wuhan seafood market, as Daszak has argued, and that it seemed too well adapted to humans to have arisen naturally. However, the story failed to garner much attention, similarly to a little noticed investigative report by the Associated Press in December that exposed how the Chinese government was clamping down on research into covid-19’s origins.

In January this year, New York magazine ran a sprawling story detailing how the pandemic could have started with a leak from the lab in Wuhan. The hypothetical scenario: “SARS-CoV-2, the virus that causes covid-19, began its existence inside a bat, then it learned how to infect people in a claustrophobic mine shaft, and then it was made more infectious in one or more laboratories, perhaps as part of a scientist’s well-intentioned but risky effort to create a broad-spectrum vaccine.” Scientists and their media allies swiftly criticised the article.

But mainstream outlets from the New York Times to the Washington Post are now treating the lab leak hypothesis as a worthy question, one to be answered with a serious investigation. In a recent interview with the New York Times , Shi denied that her lab was ever involved in “gain of function” experiments ( box 1 ) that enhance a virus’s virulence. But the newspaper reported that her lab had been involved in experiments that altered the transmissibility of viruses, alongside interviews with scientists who said that far more transparency was necessary to determine the truth of SARS-CoV-2’s origins. 17

What is “gain of function” research?

After two teams genetically tweaked the H5N1 avian flu virus in 2011 to make it more transmissible in mammals, biosafety experts voiced concerns about “gain of function” research—experimental research that involves altering microbes in ways that change their transmissibility, pathogenicity, or host range.

In the Bulletin of the Atomic Scientists in 2012, Lynn Klotz predicted an 80% chance that a leak of a potential pandemic pathogen would occur sometime in the next 12 years. Two years later a Harvard epidemiologist, Marc Lipsitch, founded the Cambridge Working Group to lobby against such experiments.

At that time, three safety lapses involving dangerous pathogens led to a safety crackdown at the US Centers for Disease Control and Prevention. Lipsitch later argued in 2018 that the release of such a pathogen would “lead to global spread of a virulent virus, a biosafety incident on a scale never before seen.”

Gain of function research was briefly paused because of these concerns, although critics debate as to when it restarted. For more than a decade, scientists at the Wuhan Institute of Virology have been discovering coronaviruses in bats in southern China and bringing them back to their lab for gain of function research, to learn how to deal with such a deadly virus should it arise in nature.

The closest known relative of the SARS-CoV-2 virus was found in a region of China almost 1000 miles from the Wuhan Institute of Virology—yet the pandemic apparently started in Wuhan. Biosafety experts have noted that lab leaks are common but rarely reported, as hundreds of lab accidents had happened in the US alone. 27

Two major events are probably responsible for the media’s change in tune. First, Trump was no longer president. Because Trump had said that the virus could have come from a Wuhan lab, Daszak and others used him as a convenient foil to attack their critics. But the framing of the lab leak hypothesis as a partisan issue was harder to sustain after Trump left the White House.

Second, after months of negotiation the Chinese government finally allowed the World Health Organization to come to Wuhan and investigate the pandemic’s origin. But in January 2021 WHO, which included Daszak on the team, returned with no evidence that the virus had arisen through natural spill-over. 18 More worryingly, members were allowed only a few hours of supervised access to the Wuhan Institute of Virology.

The White House then released a statement making clear that it did not trust China’s propaganda denying that the virus could have come from one of the country’s labs. “We have deep concerns about the way in which the early findings of the covid-19 investigation were communicated and questions about the process used to reach them,” said the statement. “It is imperative that this report be independent, with expert findings free from intervention or alteration by the Chinese government.”

The following month the Washington Post editorial board called for an open and transparent investigation of the virus’s origins, highlighting Shi’s experiments with bat coronaviruses that were genetically very similar to the one that caused the pandemic. 19 It asked, “Could a worker have gotten infected or inadvertent leakage have touched off the outbreak in Wuhan?” The Wall Street Journal , citing a US intelligence document, recently reported that three Wuhan Institute of Virology researchers were admitted to hospital in November 2019. 20

To follow any US financial ties and to better understand how the pandemic started, Republicans have launched investigations of government agencies that fund coronavirus research, and one investigative committee has sent a letter to Daszak at EcoHealth Alliance demanding that he turn over documents. Meanwhile, Senate Republicans and Democrats have started to discuss an independent investigation of the virus’s origins.

A hard truth to swallow

The growing tendency to treat the lab leak scenario as worthy of serious investigation has put some reporters on the defensive. After Robert Redfield, former director of the Centers for Disease Control and Prevention, appeared on CNN in March, Scientific American ’s editor in chief, Laura Helmuth, tweeted, “On CNN, former CDC director Robert Redfield shared the conspiracy theory that the virus came from the Wuhan lab.” The following day, Scientific American ran an essay calling the lab leak theory “evidence free.” And a week later a Nature reporter, Amy Maxmen, labelled the idea that the virus could have leaked from a lab as “conjecture.”

Helmuth did not respond to questions from The BMJ .

Some media outlets have attempted to justify their past reporting about the lab leak hypothesis as simply a matter of tracking a “scientific consensus” which, they say, has now changed. Vox posted an erratum noting, “Since this piece was originally published in March 2020, scientific consensus has shifted.”

The “scientific consensus” argument does not sit well with David Relman, a microbiologist at Stanford University, California. “We can’t even begin to talk about a consensus other than a consensus that we don’t know [the origins of SARS-CoV-2],” he recently told the Washington Post . 21

A year lost

While the narrative took months to change in the media, several high profile intelligence sources had treated the lab leak theory seriously from early on. In April 2020, Avril Haines joined two other former deputy directors of the Central Intelligence Agency to write an essay in Foreign Policy asking, “To what extent did the Chinese government misrepresent the scope and scale of the epidemic?” 22 A week later, one of the former intelligence officials who wrote that essay gave similar quotes to Politico .

Ignoring these early warnings led to a year of biased, failed reporting, says Wade. “They didn’t question what their sources were saying,” he says of the reporters who helped to sell the conspiracy theory narrative to the public. “That is the simple explanation for this phenomenon.”

An impartial, credible investigation?

As the news media scramble to correct and reflect on what went wrong with nearly a year of reporting, the episode has also highlighted quality control issues at the ubiquitous “fact checking” services.

Prominent outlets such as PolitiFact 23 and FactCheck.org 24 have added editor’s notes to pieces that previously “debunked” the idea that the virus was created in a lab or could have been bioengineered—softening their position to one of an open question that is “in dispute.” For almost a year Facebook sought to control misinformation by banning stories suggesting that the coronavirus was man made. After renewed interest in the virus’s origin, Facebook lifted the ban. 25

Whether a credible investigation will be made into the lab leak scenario remains to be seen. WHO and the Lancet both launched investigations last year ( box 2 ), but Daszak was involved in both, and neither has made significant progress.

September Weeks before the pandemic erupts, Jeremy Farrar (Wellcome Trust) and Anthony Fauci (US National Institutes of Health; NIH) help oversee a World Health Organization report highlighting an “increasing risk of global pandemic from a pathogen escaping after being engineered in a lab”

November Three researchers from the Wuhan Institute of Virology are admitted to hospital, says a previously undisclosed US intelligence document reported by the Wall Street Journal on 23 May 2021

31 December WHO is notified of cases of pneumonia of unknown aetiology in Wuhan City

1 February Jeremy Farrar holds a teleconference with Anthony Fauci and others to discuss the outbreak’s origins

6 February A commentary from Chinese researchers based in Wuhan, arguing that “the killer coronavirus probably originated from a laboratory in Wuhan,” is posted and later removed from ResearchGate (the user account “Botao Xiao” is also deleted)

19 February An open letter is published in the Lancet from 27 scientists including Peter Daszak and Jeremy Farrar, who “strongly condemn conspiracy theories suggesting that covid-19 does not have a natural origin”

19 February Science magazine reports: “Scientists ‘strongly condemn’ rumors and conspiracy theories about origin of coronavirus outbreak,” quoting Daszak as saying, “We’re in the midst of the social media misinformation age, and these rumors and conspiracy theories have real consequences, including threats of violence that have occurred to our colleagues in China.”

22 February New York Post publishes an article by a China scholar arguing that “coronavirus may have leaked from a lab”—subsequently censored by Facebook

6 March Kristian Andersen (Scripps Research Institute) thanks Jeremy Farrar (Wellcome), Anthony Fauci (NIH), and Francis Collins (NIH) “for your advice and leadership as we have been working through the SARS-CoV-2 ‘origins’ paper.” The paper is published on 17 March in Nature Medicine and states, “Our analyses clearly show that SARS-CoV-2 is not a laboratory construct or a purposefully manipulated virus.”

24 April NIH abruptly cuts funding to EcoHealth Alliance, allegedly on President Trump’s order

28 April Three former US intelligence agents write in Foreign Policy asking whether the virus emerged from nature or escaped from a Chinese lab

21 May New York Times depicts the Wuhan Institute of Virology as a victim of “conspiracy theories”

27 May Nature reports the lab leak hypothesis as “coronavirus misinformation” and “false information”

8 June The science magazine Undark reports that the lab leak is a conspiracy theory “that’s been broadly discredited”

30 December Associated Press investigation finds documents from March 2020 showing how Beijing has shaped and censored research into the origins of SARS-CoV-2

February Facebook places warning on an article by Ian Birrell about the origins of covid-19. Facebook says that these warnings reduce article viewership by 95%

13 February Jake Sullivan, US national security adviser, expresses “deep concerns” about WHO’s covid-19 investigation, calling on China to be more transparent

March Washington Post calls for serious investigations of the lab leak hypothesis

30 March WHO releases a report on its investigation into the origins of covid-19, listing the lab leak as least likely of the possible scenarios considered. Hours earlier, WHO’s director general, Tedros Adhanom Ghebreyesus, acknowledged that the lab leak hypothesis should “remain on the table” and called for a more extensive probe

30 March The US, Australian, Japanese, Canadian, UK, and other governments express concern over WHO’s investigation and call for “transparent and independent analysis and evaluation, free from interference and undue influence”

26 May Facebook lifts its ban on posts referencing the lab leak hypothesis

In recent weeks, several high profile scientists who once denigrated the idea that the virus could have come from a lab have made small steps into demanding an open investigation of the pandemic’s origin.

The NIH’s director, Francis Collins, said in a recent interview, “The Chinese government should be on notice that we have to have answers to questions that have not been answered about those people who got sick in November who worked in the lab and about those lab notebooks that have not been examined.” He added, “If they really want to be exonerated from this claim of culpability, then they have got to be transparent.” 26

But the nature of this investigation has still not been decided.

Competing interests: I am paid by various media outlets for journalism stories and consult part time for a non-profit institute focused on brain disorders. I run a newsletter called the Disinformation Chronicle .

Provenance and peer review: Commissioned, not externally peer reviewed.

This article is made freely available for use in accordance with BMJ's website terms and conditions for the duration of the covid-19 pandemic or until otherwise determined by BMJ. You may use, download and print the article for any lawful, non-commercial purpose (including text and data mining) provided that all copyright notices and trade marks are retained.

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• ↵ Opinion: The WHO needs to start over in investigating the origins of the coronavirus. Washington Post 2021 Mar 6. https://www.washingtonpost.com/opinions/global-opinions/the-who-needs-to-start-over-in-investigating-the-origins-of-the-coronavirus/2021/03/05/6f3d5a0e-7de9-11eb-a976-c028a4215c78_story.html
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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.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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.

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MINI REVIEW article

The hygiene hypothesis – learning from but not living in the past.

• 1 Comprehensive Biobank Marburg, Medical Faculty, Philipps University of Marburg, Comprehensive Biobank Marburg, Marburg, Germany
• 2 German Center for Lung Research (DZL), Marburg, Germany
• 3 German Biobank Alliance, Marburg, Germany
• 4 Institute for Pathology, Medical Faculty, Institute for Pathology, Philipps University of Marburg, Marburg, Germany
• 5 Translational Inflammation Research Division & Core Facility for Single Cell Multiomics, Medical Faculty, Biochemical Pharmacological Center, Philipps University of Marburg, Marburg, Germany

Postulated by Strachan more than 30 years ago, the Hygiene Hypothesis has undergone many revisions and adaptations. This review journeys back to the beginnings of the Hygiene Hypothesis and describes the most important landmarks in its development considering the many aspects that have refined and generalized the Hygiene Hypothesis over time. From an epidemiological perspective, the Hygiene Hypothesis advanced to a comprehensive concept expanding beyond the initial focus on allergies. The Hygiene Hypothesis comprise immunological, microbiological and evolutionary aspects. Thus, the original postulate developed into a holistic model that explains the impact of post-modern life-style on humans, who initially evolved in close proximity to a more natural environment. Focusing on diet and the microbiome as the most prominent exogenous influences we describe these discrepancies and the resulting health outcomes and point to potential solutions to reestablish the immunological homeostasis that frequently have been lost in people living in developed societies.

Last year we celebrated the 30th anniversary of the Hygiene Hypothesis. Since Strachan framed the Hygiene Hypothesis in 1989 ( 1 ) his fundamental idea to explain the origins of allergic diseases development has survived the test of time. The basic idea of how humans, their microbiota, and a continuously modernizing environment have interacted to drive immune dysregulation has persisted and become part of the popular imagination. Here, we aim to provide an editorial overview on the history of the Hygiene Hypothesis and related topics to offer a framework for the articles collected in the special edition research topic “The Hygiene Hypothesis and its Immunological Implications.”

A Chronological Overview

The epidemiological basis for the Hygiene Hypothesis became apparent long before the Hygiene Hypothesis was postulated. Two simple observations were made in the 1960s and in the 1970s. First, a Swedish study described differences in the prevalence of asthma and socio-medical conditions between populations living in urban or rural sites ( 2 ). A few years later, in a population-based study conducted in Saskatchewan, Canada, showed that allergies were less frequent in native tribes living traditionally in rural sites compared to Caucasian Canadians living in urban habitats ( 3 ). Moreover, the authors postulated that frequent bacterial infections in childhood might be responsible for the inverse association with allergic diseases. Strachan's observations made in the late 80s in a British population corroborated these findings and he later named this concept “Hygiene Hypothesis” in 2000. Briefly, Strachan suggested that transfer of early childhood infections between siblings is associated with protection against allergies later in life ( 4 ).

The hypothesis was further substantiated and extended by studies that compared asthma and allergy prevalence directly after the “Fall of the Iron Curtain” between Western and Eastern Germany, a decade later ( 5 , 6 ). Interestingly, these studies triggered a paradigm shift in allergy research. Until then, environmental pollution was broadly regarded as the leading force for allergy development. Environmental data clearly indicated a higher level of pollution by industrial emissions in Eastern Germany compared to the Western part and the study team therefore hypothesized that the prevalence of allergic diseases was higher in children from Eastern Germany. Surprisingly, the researchers found their hypothesis disproved, as children in Western Germany showed a higher prevalence of allergies. Hence, it was postulated that other exposures than pollutants influence the development of atopic diseases. Socio-demographic and –economic factors, as well as household hygiene turned out to be further discriminatory factors between both parts of German population. Improved sanitation and hygiene were positively associated with atopic diseases. Another decade later a follow-up further validated this hypothesis and found life-style differences and the prevalence of atopic diseases began to equilibrate within 10 years after the reunification. In consequence, the Hygiene Hypothesis became the leading postulate to explain underlying relationships and mechanisms for the development of allergic diseases in a societal context ( 6 ).

Based on this paradigm shift, Rook published the “Old Friends-Hypothesis” which argues that infectious diseases have a long co-evolutionary history with human development, and appropriate levels of exposure to these microorganisms early in life might protect against immune deviation and allergic diseases. These early-life exposures to potential pathogens might educate the developing immune system from a type-2-dominated in utero -milieu toward a more defensive T helper (h)1 response ( 7 ).

The next milestone involved findings obtained from the so-called “Alpine farm studies” conducted at the turn of the millennium. Von Mutius and Braun-Fahrländer recognized the unique situation that the Alpine traditional farming environment represents a socio-cultural and ecological niche which significantly differs from the post-modern and urbanized life-style. In a number of epidemiological studies they identified traditional farming characteristics such as consumption of unprocessed farm milk and close contact with farm animals to act allergoprotective and found these parameters to be associated with a higher microbial load. These Alpine farm studies added substantial evidence to Strachan's basic idea and led to a broader view and understanding of the relationship between human health and (early life) exposure to microbes ( 8 , 9 ).

Further evidence was added by studies conducted in Northern Europe. In the late 1990s studies conducted in Scandinavian und Baltic children described microbial factors to be associated with a lower prevalence of allergic diseases in the Eastern countries ( 10 – 12 ). Next, the Karelia Study, conducted on both sides of the Finnish-Russian border, addressed the impact of the environmental microbial burden on the development of allergic diseases in Finnish and Russian Karelian children that share the same ethnic background but have different life-styles ( 13 ). These studies corroborated the Alpine farm studies and point to the microbial environment as a major factor in allergy development.

Furthermore, these studies demonstrate that the diversity and the richness of an immune-stimulating microbial world in human habitats is crucial to establish a competent, tolerogenic and defensive immune system configuration while absence or depletion of those stimuli as found in post-modern environments foster immune deviation and development of allergic diseases ( 14 ).

Moreover, two relevant studies [the cross-sectional study “Prevention of Allergy Risk factors for Sensitization In children related to Farming and Anthroposophic Lifestyle (PARSIFAL)” and the multi-center, pregnancy/birth cohort study “Protection against Allergy: Study in Rural Environments (PASTURE)”] support the idea that the “window of opportunity” in which the appropriate education of the immune system starts already in the mother's womb ( 15 – 17 ). The PARSIFAL Study demonstrated that maternal exposure to a farm environment rich in microbial compounds is inversely associated with the development of atopic sensitization and correlated with an upregulation of receptors of the innate immune system in the offspring at school age ( 15 ). Further, maternal farm activities during pregnancy were shown to modulate cord blood cytokines and allergen-specific immunoglobulin responses toward a Th1 pattern ( 16 , 17 ). These findings are in line with the Barker theory ( 18 ), postulating that pathological pathways occurring in adolescence and adulthood are paved already in prenatal life.

The Hygiene Hypothesis and the Bacterial World

Even before high-throughput sequencing techniques were established that allow a deeper view into the microbial world on our body surfaces, Noverr and Hufnagle proclaimed the “Microbiota Hypothesis” by which they claimed the microbiota to be indispensable for developing and maintaining a tolerogenic immune status ( 19 ). A similar idea concept was proposed earlier by Holt, Sly and Björkstén ( 20 ). The rediscovery of the microbiota and its powerful metabolic and immunologic interplay with the mucosal surfaces of the host underlined and complemented the principals of this basic idea ( 21 , 22 ). Microbiome research has made significant achievements over the past 15 years; here we can emphasize only a few aspects that might be relevant in the context of the Hygiene Hypothesis and the development of allergies.

Phylogenetic Impacts

An intriguing concept to better understand the complex symbiotic interplay at organ surfaces was suggested by McFall-Ngai in 2007. In her evolutionary perspective she shed light on findings made in invertebrates which not only lack an endoskeleton but also an adaptive immune system. Thus, invertebrates have to exclusively rely on their innate immune system, which to our current understanding, lacks an immunological memory. Analyses of the intestinal microbiota in such animals have shown—in contrast to vertebrates—a rather low diversity in the community of their microbial residents. Only a handful of strains could be identified as stable colonizers on the gastrointestinal surfaces while most bacteria travel through as transient visitors. Some invertebrates, like insects, separate bacterial colonies from epithelial host cells by a peritrophic matrix composed of chitin and other compounds ( 24 ). During the course of evolution, the microbial colonization of epithelia started to get more complex and in turn the host was challenged to develop new strategies to manage these diversifying communities. To permanently recognize a specific bacterium as beneficial or harmful, an adaptive immune response that provides an immunological memory over generations of immune cells was needed. Mutual adaption of both partners, the bacterial community, as well as the complex network of adaptive immune cells, led to a sophisticated metabolic and immunologic interplay with a highly digestive and defensive performance. This symbiosis is based on early education of the host's immune cells by a diverse microbial community to successfully discriminate dangerous pathogens from beneficial symbionts and own healthy cells. Finally McFall-Ngai stated, that complex systems might be prone to failure and allergies and autoimmune disorders might be a consequence of this ( 23 ).

Ontogenetic Impacts

A number of recently published reports substantiated the impact of the early life microbiota on immune maturation [recently reviewed in ( 25 )] and the development of allergic disorders in early infancy [recently reviewed in ( 26 )]. The developmental starting point of the infant gut microbiota is still unknown, but undoubtedly, the process of delivery seems to be a key point in the development of the neonatal microbiota ( 27 ). Meconium, the neonate's first intestinal discharge, was shown to contain various bacterial strains indicating that the perinatal gut is colonized by bacteria ( 28 , 29 ). In a landmark study, Dominguez-Bello et al. reported that the neonatal microbiota differs between vaginally born infants and neonates delivered by Caesarian (C)-section. The authors found a high abundance of Bacteroides, Bifidobacterium , and Lactobacillus spec . in meconium samples obtained from vaginally delivered newborns, while Staphylococcus, Streptococcus, Corynebacterium , and Propionibacterium spp . were found predominantly in meconium samples of C-section born neonates ( 30 ).

Colonization of the neonate's colon by Lactobacilli and Bifidobacteria transferred from the maternal vaginal compartment during vaginal passage might provide advantages for the newborn due to the metabolic properties of these bacteria that foster the adaptation to milk-based feeding. These bacteria are capable of metabolizing breast milk-derived lactose and human milk oligosaccharides (HMOS) ( 31 ) and were shown to provide immune-modulating short chain fatty acids (SCFAs) ( 32 ) and conjugated trans-linoleic acids (tCLAs) ( 33 ), which are shown to reduce pro-inflammatory eicosanoid production by regulating the transcription of cyclooxygenase 2 (COX-2) ( 34 ) and to induce anti-inflammatory M2-macrophage differentiation ( 35 ).

However, how sustainable and decisive are these mode of delivery-associated differences beyond the neonatal age? Chu et al. recently showed that function and composition of the microbiota significantly diversifies in all body sites within the first 6 weeks of life, resembling the corresponding maternal body site microbiota at this time point. Infant's mode of delivery or other prenatal factors seems to have no impact on this development ( 36 ). Data from the Copenhagen Prospective Studies on Asthma in Childhood 2010 (COPSAC 2010 ) cohort underlined the importance of the maturation of the microbiota on the further development of the gut microbiome and the risk of asthma later in life. In that study, Stockholm et al. compared the gut microbiome of vaginally and C-section delivered infants from birth to 1 year of life in the context of asthma development at school age. Marked differences between C-section and vaginally delivered infants were observed by 1 week and by 1 month of life, but only minor differences between these groups were found by 1 year of age. An increased risk for school-age asthma was only observed in a subgroup of C-section-born infants that maintained the C-section-associated composition for at least 1 year. The authors conclude that vaginal delivery and/or subsequent maturation of the infant microbiota might support a more robust and stable microbiota in the offspring that is more adaptive to the challenges later in life ( 37 ). Further exposure to the maternal microbiota ( 38 ), as well as nutritional impacts (e.g., cessation of breastfeeding) ( 39 ) within the first month of life, might foster the maturation of the gut microbiome in early infancy.

Nutritional Impacts

How is the microbiota linked to the rising atopic epidemic observed in the recent decades? A recently published study conducted in indigenous tribes living in the Brazilian Amazonas-Orinoco Basin may help to answer this question ( 40 ). In this study the gut microbiome of the semi-nomadic gatherer/hunter people of the Yanomami who maintained a primitive close-to-nature life-style was compared to subjects representing populations that are characterized by a westernized or non-ancestral life-style in rural and urban settings. The Yanomami microbiota was significantly more diverse than those of the westernized counterparts. Moreover, an additional study comparing Venezuelan with Brazilian Yanomami indicated a high level of adaptability to specific environmental conditions of the microbiota in these peoples. While a high taxonomic diversity was found in both sub-tribes, the composition of microbiota was significantly different ( 41 ). These findings point to environmental and life-style factors that influence the composition of the microbiota the absence of which may thus foster the loss of taxonomic and metabolic diversity in westernized societies ( 42 ).

Diet is one of the most prominent environmental factors that differ between modern and ancient life-styles. While dietary habits in indigenous people such as the Yanomami strongly depend on the sometimes limited food supply due to seasonal cycling, people living in developed societies have access to high in calories food ready at any time and in abundance. Moreover, diet in indigenous cultures is often based on high-fiber products derived from plants that are easy to culture such as plantain, manioc or sweet potatoes, all rich in inulin ( 43 ). High-fiber diet and, in particular inulin, is known as an effective enhancer of beneficial bacteria such as Bifidobacteria in the colon that stabilize gut homeostasis ( 44 ). Translating these findings into a clinical approach, McLoughlin et al. applied soluble inulin to asthmatics in a short-term placebo-controlled-trial and could report an array of beneficial effects in patients orally treated with inulin. In comparison to the placebo group, inulin-treated patients displayed a significantly reduced number of eosinophils in the sputum and, overall, reported a significantly improved asthma control. Inhibition of histone deacetylase 9 (HDAC9) in sputum cells upon a combined application of inulin and a multi-strain probiotic mixture of Lactobacillus acidophilus, Lactobacillus rhamnosus GG and Bifidobacterium animalis subspecies lactis indicated that epigenetic pathways are involved in the mechanisms by which lactic acid bacteria modulate host responses in combination with the prebiotic gavage ( 45 ).

A number of recently recognized metabolites released by beneficial symbiotic bacteria convey immunomodulatory effects, mainly in the gut but also on other mucosal surfaces ( 46 ).

In particular, SCFAs derived from dietary fibers and released in the lumen of the colon contribute to immune modulation and inhibition of pro-inflammatory cytokines when absorbed by gut epithelial cells ( 47 ). By binding to chemoattractant G protein 43 receptor, SCFAs are capable of regulating inflammatory responses ( 48 ) as shown for intestinal inflammation ( 49 ). Tryptophan, an amino acid produced by an array of beneficial microorganisms, is degraded to indole derivatives which may bind to the aryl hydrocarbon receptor (AHR) and by this regulate the activity of immune cells at the epithelial barrier. That involves AHR-dependent differentiation of regulatory T cells associated with anti-inflammatory IL-10 expression. Further, Th2-cells are inhibited on the transcription factor level in favor of a Th1 response ( 50 ).

A number of beneficial bacteria contribute to the orchestration of T cell subsets at the gut epithelial barrier. Bacteroides sp . and Clostridium clusters IV and XIVa colonizing the gut epithelium are known to stimulate intestinal epithelial cells to release thymic stromal lymphopoietin (TSLP), transforming growth factor (TGF)-ß and interleukin (IL)-25 which in combination may induce tolerogenic effects in dendritic cells (DCs) ( 51 ), e.g., by secretion of TGF-ß and retinoic acid. Both factors initiate differentiation of naïve T cells to regulatory T cells upon activation of the nuclear transcription factor forkhead box P (FoxP3) ( 52 ). These regulatory mechanisms are challenged by “pathobionts” or other damage factors. In presence of these stressors, overexpansion of Th1, Th2 and Th17 effector cell subsets might result in an inflammatory response in the infected organ or, by migration of these cells, at distant sites. Namely, Clostridium difficile , which is associated with wheezing and atopic sensitization, was shown to initially disturb the intestinal balance when acquired early in childhood ( 53 ).

Traveling from the gastrointestinal to the respiratory tract the microbiota established in the lung might also play a role in the development of allergic disorders, namely of allergic asthma. Though the gut is known to play a major role in establishing and regulating immune defense mechanisms, the “gut-lung axis” alone might not completely explain the rise of allergic asthma ( 54 ). As many studies focused on the lung microbiome, it has become clear that there is a strong relationship between frequently inhaled environmental microbes, microbial colonization of the respiratory tract, and the prevalence of allergic asthma ( 55 ). For example, results from the “Multidisciplinary Study to Identify the Genetic and Environmental Causes of Asthma in the European Community (GABRIEL) Advanced Studies (GABRIELA)” study suggested a transfer of built-environment-associated bacteria into the respiratory tract. Indoor dust samples from farm houses and nasal swabs from farm children displayed a higher bacterial diversity than those samples collected in rural non-farm children ( 56 ). New evidence was added recently by studies conducted in the Finnish part of the PASTURE-study. Kirjavainen et al. reported that the ecological diversity of the so-called “indoor microbiota” is inversely linked to the prevalence of allergic asthma. Substantiating former farm studies, this report further validated the hypothesis that microbial diversity and composition in the natural environment is linked to a reduced risk of early-onset allergic asthma and that traditional farming is a proxy for this effect ( 57 ).

But what are the cellular and molecular mechanisms associated with high microbial diversity? Interestingly, the farm studies consistently showed an inverse association between a highly diverse environmental microbiota and allergic asthma, but this did not account to other allergic manifestations such as hay fever or atopic sensitization. On the other hand, endotoxin exposure protects against atopy but fosters the risk of non-allergic asthma and early onset of wheeze when inhaled in higher concentrations. These findings derived from the farm studies still challenge the Hygiene Hypothesis and might point out that microbial colonization and exposure to microbial compounds have to be considered separately ( 58 ). Integration of beneficial environmental bacteria into the microbial community of the respiratory tract leads to a tolerogenic mucosal symbiosis that establishes a local T-cell balanced anti-inflammatory milieu at the epithelium, probably enhanced by a well-balanced gut microbiota. Endotoxins are potent activators of innate TLR-signaling and can attenuate B cell driven sensitization and formation of IgE-antibodies ( 59 ). Already in 2003, Vercelli postulated a switch from Th2-driven allergic responses at low endotoxin exposure to a pronounced Th1 response in the lung under high levels of environmental endotoxin. This might explain the elevated prevalence of non-allergic asthma in environments overloaded with endotoxin ( 60 ).

Conclusions

The many aspects and facets of the Hygiene Hypothesis have been supported by concepts and findings coming from a variety of scientific disciplines such as epidemiology, immunology, microbiology and anthropology. Within the last three decades we obtained a multiplicity of new insights into the complexity and plasticity of T cell networks which led us to recognize the complexity and significance of a powerful and well-regulated adaptive immune response in relation to exogenous factors ( 61 ). Early developmental findings characterizing pre and postnatal life events highlighted the initial role of the innate immune system as an early warning system that orchestrates, educates and shapes subsequent immune responses ( 62 , 63 ). Evidence from evolutionary biology and anthropology enabled us to understand how host-environment interactions are refined throughout evolutionary adaption ( 58 , 64 ). Microbiology added fundamental knowledge about the micro-ecosystem that is established throughout the human body as a unique symbiosis between humans and microbes. And finally, coming back to the introductory statement, epidemiological observations such as those initially made by Strachan and von Mutius about 30 years ago still challenge and refine the hypothesis.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

This work was funded by the Library of the Philipps-University of Marburg.

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.

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Keywords: hygiene hypothesis, allergy, asthma, immune tolerance, T cell-response, microbiome

Citation: Pfefferle PI, Keber CU, Cohen RM and Garn H (2021) The Hygiene Hypothesis – Learning From but Not Living in the Past. Front. Immunol. 12:635935. doi: 10.3389/fimmu.2021.635935

Received: 30 November 2020; Accepted: 17 February 2021; Published: 16 March 2021.

Reviewed by:

Copyright © 2021 Pfefferle, Keber, Cohen and Garn. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Petra I. Pfefferle, petraina.pfefferle@uni-marburg.de

Disclaimer: 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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The covid-19 lab leak hypothesis: did the media fall victim to a misinformation campaign?

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• PMID: 34244293
• DOI: 10.1136/bmj.n1656
• The covid-19 lab leak hypothesis: did the media fall victim to a misinformation campaign? [No authors listed] [No authors listed] BMJ. 2021 Jul 12;374:n1774. doi: 10.1136/bmj.n1774. BMJ. 2021. PMID: 34253536 No abstract available.

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Conflict of interest statement

Competing interests: I am paid by various media outlets for journalism stories and consult part time for a non-profit institute focused on brain disorders. I run a newsletter called the Disinformation Chronicle.

• Covid-19 laboratory leak hypothesis: how a few kept the many from considering alternative possibilities. Shin GY, Manuel R. Shin GY, et al. BMJ. 2021 Aug 13;374:n2003. doi: 10.1136/bmj.n2003. BMJ. 2021. PMID: 34389555 No abstract available.
• Covid-19 laboratory leak hypothesis: extraordinary claims demand extraordinary evidence. Donnelly O. Donnelly O. BMJ. 2021 Aug 13;374:n2006. doi: 10.1136/bmj.n2006. BMJ. 2021. PMID: 34389556 No abstract available.
• Covid-19 laboratory leak hypothesis: a convenient scapegoat. Bannon C. Bannon C. BMJ. 2021 Aug 13;374:n2010. doi: 10.1136/bmj.n2010. BMJ. 2021. PMID: 34389615 No abstract available.

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An Introduction to Statistics: Understanding Hypothesis Testing and Statistical Errors

Priya ranganathan.

1 Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Mumbai, Maharashtra, India

2 Department of Surgical Oncology, Tata Memorial Centre, Mumbai, Maharashtra, India

The second article in this series on biostatistics covers the concepts of sample, population, research hypotheses and statistical errors.

How to cite this article

Ranganathan P, Pramesh CS. An Introduction to Statistics: Understanding Hypothesis Testing and Statistical Errors. Indian J Crit Care Med 2019;23(Suppl 3):S230–S231.

Two papers quoted in this issue of the Indian Journal of Critical Care Medicine report. The results of studies aim to prove that a new intervention is better than (superior to) an existing treatment. In the ABLE study, the investigators wanted to show that transfusion of fresh red blood cells would be superior to standard-issue red cells in reducing 90-day mortality in ICU patients. 1 The PROPPR study was designed to prove that transfusion of a lower ratio of plasma and platelets to red cells would be superior to a higher ratio in decreasing 24-hour and 30-day mortality in critically ill patients. 2 These studies are known as superiority studies (as opposed to noninferiority or equivalence studies which will be discussed in a subsequent article).

SAMPLE VERSUS POPULATION

A sample represents a group of participants selected from the entire population. Since studies cannot be carried out on entire populations, researchers choose samples, which are representative of the population. This is similar to walking into a grocery store and examining a few grains of rice or wheat before purchasing an entire bag; we assume that the few grains that we select (the sample) are representative of the entire sack of grains (the population).

The results of the study are then extrapolated to generate inferences about the population. We do this using a process known as hypothesis testing. This means that the results of the study may not always be identical to the results we would expect to find in the population; i.e., there is the possibility that the study results may be erroneous.

HYPOTHESIS TESTING

A clinical trial begins with an assumption or belief, and then proceeds to either prove or disprove this assumption. In statistical terms, this belief or assumption is known as a hypothesis. Counterintuitively, what the researcher believes in (or is trying to prove) is called the “alternate” hypothesis, and the opposite is called the “null” hypothesis; every study has a null hypothesis and an alternate hypothesis. For superiority studies, the alternate hypothesis states that one treatment (usually the new or experimental treatment) is superior to the other; the null hypothesis states that there is no difference between the treatments (the treatments are equal). For example, in the ABLE study, we start by stating the null hypothesis—there is no difference in mortality between groups receiving fresh RBCs and standard-issue RBCs. We then state the alternate hypothesis—There is a difference between groups receiving fresh RBCs and standard-issue RBCs. It is important to note that we have stated that the groups are different, without specifying which group will be better than the other. This is known as a two-tailed hypothesis and it allows us to test for superiority on either side (using a two-sided test). This is because, when we start a study, we are not 100% certain that the new treatment can only be better than the standard treatment—it could be worse, and if it is so, the study should pick it up as well. One tailed hypothesis and one-sided statistical testing is done for non-inferiority studies, which will be discussed in a subsequent paper in this series.

STATISTICAL ERRORS

There are two possibilities to consider when interpreting the results of a superiority study. The first possibility is that there is truly no difference between the treatments but the study finds that they are different. This is called a Type-1 error or false-positive error or alpha error. This means falsely rejecting the null hypothesis.

The second possibility is that there is a difference between the treatments and the study does not pick up this difference. This is called a Type 2 error or false-negative error or beta error. This means falsely accepting the null hypothesis.

The power of the study is the ability to detect a difference between groups and is the converse of the beta error; i.e., power = 1-beta error. Alpha and beta errors are finalized when the protocol is written and form the basis for sample size calculation for the study. In an ideal world, we would not like any error in the results of our study; however, we would need to do the study in the entire population (infinite sample size) to be able to get a 0% alpha and beta error. These two errors enable us to do studies with realistic sample sizes, with the compromise that there is a small possibility that the results may not always reflect the truth. The basis for this will be discussed in a subsequent paper in this series dealing with sample size calculation.

Conventionally, type 1 or alpha error is set at 5%. This means, that at the end of the study, if there is a difference between groups, we want to be 95% certain that this is a true difference and allow only a 5% probability that this difference has occurred by chance (false positive). Type 2 or beta error is usually set between 10% and 20%; therefore, the power of the study is 90% or 80%. This means that if there is a difference between groups, we want to be 80% (or 90%) certain that the study will detect that difference. For example, in the ABLE study, sample size was calculated with a type 1 error of 5% (two-sided) and power of 90% (type 2 error of 10%) (1).

Table 1 gives a summary of the two types of statistical errors with an example

Statistical errors

In the next article in this series, we will look at the meaning and interpretation of ‘ p ’ value and confidence intervals for hypothesis testing.

Source of support: Nil

Conflict of interest: None

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By holding ancient wisdom to the test of modern psychology, Haidt extracts lessons on how we can train our brains to be more optimistic, build better relationships and achieve a sense of balance. He also explores how we can overcome the obstacles to well-being that we place in our own way. In this uplifting and empowering book, Haidt draws on sources as diverse as Buddha, Benjamin Franklin and Shakespeare to show how we can find happiness and meaning in life. 'I don't think I ever read a book that laid out the contemporary understanding of the human condition with such simple clarity and sense.' 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Popular highlight Product details. Publisher ‏ : ‎ Random House Business (January 7, 2021) Language ‏ : ‎ English ISBN-10 ‏ : ‎ 1847943063 ISBN-13 ‏ : ‎ 978-1847943064 Item Weight ‏ : ‎ 7.9 ounces Dimensions ‏ : ‎ 5.08 x 0.75 x 7.8 inches #257 in Religion & Philosophy (Books) #520 in Emotional Mental Health #3,658 in Motivational Self-Help (Books) About the author Jonathan haidt. Jonathan Haidt is the Thomas Cooley Professor of Ethical Leadership at New York University's Stern School of Business. He received his Ph.D. in social psychology from the University of Pennsylvania in 1992 and then did post-doctoral research at the University of Chicago and in Orissa, India. He taught at the University of Virginia for 16 years before moving to NYU-Stern in 2011. He was named one of the "top global thinkers" by Foreign Policy magazine, and one of the "top world thinkers" by Prospect magazine. His research focuses on morality - its emotional foundations, cultural variations, and developmental course. He began his career studying the negative moral emotions, such as disgust, shame, and vengeance, but then moved on to the understudied positive moral emotions, such as admiration, awe, and moral elevation. He is the co-developer of Moral Foundations theory, and of the research site YourMorals.org. He is a co-founder of HeterodoxAcademy.org, which advocates for viewpoint diversity in higher education. He uses his research to help people understand and respect the moral motives of their enemies (see CivilPolitics.org, and see his TED talks). He is the author of The Happiness Hypothesis: Finding Modern Truth in Ancient Wisdom; The Righteous Mind: Why Good People are Divided by Politics and Religion; and (with Greg Lukianoff) The Coddling of the American Mind: How good intentions and bad ideas are setting a generation up for failure. For more information see www.JonathanHaidt.com. 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Top reviews from other countries About Amazon Investor Relations Amazon Devices Amazon Science Sell products on Amazon Sell on Amazon Business Sell apps on Amazon Become an Affiliate Advertise Your Products Self-Publish with Us Host an Amazon Hub › See More Make Money with Us Amazon Business Card Shop with Points Reload Your Balance Amazon Currency Converter Amazon and COVID-19 Your Account Your Orders Shipping Rates & Policies Returns & Replacements Manage Your Content and Devices Conditions of Use Privacy Notice Consumer Health Data Privacy Disclosure Your Ads Privacy Choices Cast & crew The Fishnet Hypothesis Miranda, a mermaid obsessed internet personality known for her quirky makeup tutorials, has placed an ad for a production assistant. Todd, the first of many to respond, has been chosen for a... Read all Miranda, a mermaid obsessed internet personality known for her quirky makeup tutorials, has placed an ad for a production assistant. Todd, the first of many to respond, has been chosen for an interview. Miranda films the interview process under the premise that she'd like her aud... Read all Miranda, a mermaid obsessed internet personality known for her quirky makeup tutorials, has placed an ad for a production assistant. Todd, the first of many to respond, has been chosen for an interview. Miranda films the interview process under the premise that she'd like her audience to connect with the person she chooses as well. During the interview, things get str... Read all Elizabeth Fields Trever Bowen 1 Critic review 8 wins & 4 nominations All cast & crew Production, box office & more at IMDbPro Did you know Trivia The Fishnet Hypothesis was filmed entirely during the Covid quarantine. Other Film Nominations - iHorrordb Horror Film Festival - Semi Finalist. User reviews April 1, 2021 (United States) Woodland Studios See more company credits at IMDbPro Technical specs Runtime 1 hour 2 minutes Related news Contribute to this page. See more gaps Learn more about contributing More to explore Recently viewed. 337 IMAGES How To Write a Hypothesis Like A Professional in 2021 Difference between Null Hypothesis and Alternative Hypothesis 2021 Hypothesis Testing Structure Summary (update 2021) research hypothesis framework, 2021 Source: Compiled literature data Lecture 05. Hypothesis and Probability 2021.pdf Week 10 Hypotheses 2021-2022 VIDEO 2. Hypothesis (12th August 2021) Concept of Hypothesis What Is A Hypothesis? Chuck Lorre Productions #233/Warner Bros Television (2008) HYPOTHESIS TESTING PROBLEM-5 USING Z TEST VIDEO-8 HYPOTHESIS TESTING PROBLEM-2 USING Z TEST VIDEO-5 COMMENTS A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles On the other hand, a research hypothesis is an educated statement of an expected outcome. ... 2021; 21 (1):162. [PMC free article] [Google Scholar] 30. Shimpuku Y, Madeni FE, Horiuchi S, Kubota K, Leshabari SC. A family-oriented antenatal education program to improve birth preparedness and maternal-infant birth outcomes: a cross sectional ... How to Write a Strong Hypothesis 6. Write a null hypothesis. If your research involves statistical hypothesis testing, you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0, while the alternative hypothesis is H 1 or H a. The COVID lab-leak hypothesis: what scientists do and don't know 08 June 2021; The COVID lab-leak hypothesis: what scientists do and don't know ... and many are calling for a deeper investigation into the hypothesis that the virus emerged from the Wuhan ... Hypothesis Testing Present the findings in your results and discussion section. Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps. Table of contents. Step 1: State your null and alternate hypothesis. Step 2: Collect data. Step 3: Perform a statistical test. Formulating Hypotheses for Different Study Designs Generating a testable working hypothesis is the first step towards conducting original research. Such research may prove or disprove the proposed hypothesis. Case reports, case series, online surveys and other observational studies, clinical trials, and narrative reviews help to generate hypotheses. ... 2021; 397 (10279):1063-1074. [PMC free ... Hypothesis: Definition, Examples, and Types 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 ... What is a Research Hypothesis: How to Write it, Types, and Examples 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. When are hypotheses useful in ecology and evolution? Hypothesis: An explanation for an observed phenomenon. Research Hypothesis: A statement about a phenomenon that also includes the potential mechanism or cause of that phenomenon. Though a research hypothesis doesn't need to adhere to this strict framework it is often best described as the "if" in an "if-then" statement. The covid-19 lab leak hypothesis: did the media fall victim to a A conspiracy to label critics as conspiracy theorists. Scientists and reporters contacted by The BMJ say that objective consideration of covid-19's origins went awry early in the pandemic, as researchers who were funded to study viruses with pandemic potential launched a campaign labelling the lab leak hypothesis as a "conspiracy theory.". A leader in this campaign has been Peter Daszak ... Full article: Editorial: Roles of Hypothesis Testing, p-Values and Abstract. The role of hypothesis testing, and especially of p-values, in evaluating the results of scientific experiments has been under debate for a long time.At least since the influential article by Ioannidis (Citation 2005) awareness is growing in the scientific community that the results of many research experiments are difficult or impossible to replicate. Facemasks in the COVID-19 era: A health hypothesis Hypothesis. On January 30, 2020, the World Health Organization (WHO) announced a global public health emergency of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) causing illness of coronavirus disease-2019 (COVID-19) .As of October 1, 2020, worldwide 34,166,633 cases were reported and 1,018,876 have died with virus diagnosis. Why hypothesis testers should spend less time testing hypotheses For almost half a century, Paul Meehl educated psychologists about how the mindless use of null-hypothesis significance tests made research on theories in the social sciences basically uninterpretable. In response to the replication crisis, reforms in psychology have focused on formalizing procedures for testing hypotheses. ... 2021-66119-007 ... Why Hypothesis Testers Should Spend Less Time Testing Hypotheses The process we taught our hypothetical student above is commonly known as the hypothetico-deductive (HD) method. Hypothetico-deductivism is "the philosophy of science that focuses on designing tests aimed at falsify-ing the deductive implications of a hypothesis" (Fidler et al., 2018, p. 238). Research Hypothesis In Psychology: Types, & Examples Examples. 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. Frontiers Strachan's observations made in the late 80s in a British population corroborated these findings and he later named this concept "Hygiene Hypothesis" in 2000. Briefly, Strachan suggested that transfer of early childhood infections between siblings is associated with protection against allergies later in life ( 4 ). Medical Hypotheses About the journal. Medical Hypotheses is a forum for ideas in medicine and related biomedical sciences. It will publish interesting and important theoretical papers that foster the diversity and debate upon which the scientific process thrives. The Aims and Scope of Medical Hypotheses are no different now from what …. View full aims & scope. The covid-19 lab leak hypothesis: did the media fall victim to a The covid-19 lab leak hypothesis: did the media fall victim to a misinformation campaign? BMJ. 2021 Jul 8:374:n1656. doi: 10.1136/bmj.n1656. Author Paul D Thacker 1 Affiliation 1 Madrid. PMID: 34244293 DOI: 10.1136/bmj.n1656 No abstract available ... Why Hypothesis Testers Should Spend Less Time Testing Hypotheses The process we taught our hypothetical student above is commonly known as the hypothetico-deductive (HD) method. Hypothetico-deductivism is "the philosophy of science that focuses on designing tests aimed at falsifying the deductive implications of a hypothesis" (Fidler et al., 2018, p. 238).An important modification to the HD method was Popper's critical rationalism (Popper, 1959 ... An Introduction to Statistics: Understanding Hypothesis Testing and HYPOTHESIS TESTING. A clinical trial begins with an assumption or belief, and then proceeds to either prove or disprove this assumption. In statistical terms, this belief or assumption is known as a hypothesis. Counterintuitively, what the researcher believes in (or is trying to prove) is called the "alternate" hypothesis, and the opposite ... Seven Challenges for the Dehumanization Hypothesis Challenge 1: comparisons to nonhuman entities are not reserved for out-groups. A key source of evidence in favor of the dehumanization hypothesis comes from real-world examples of situations in which members of certain out-groups have been compared to nonhuman entities ( Smith, 2011, 2014, 2016; Tirrell, 2012 ). Hypothesis XVI 2021 : Hypothesis XVI The HYPOTHESIS 2021 topics cover research and technological aspects on fundamentals, materials, modeling, simulation and system development for: Hydrogen production from conventional fossil sources Hydrogen production from wastes and residual biomass Electrolysis and other hydrogen production technologies from renewable sources The Happiness Hypothesis: Ten Ways to Find Happiness and Meaning in The Happiness Hypothesis by Jonathan Haidt is an outstanding book on happiness as also on meaning and purpose in life.It is uplifting as well as empowering . Haidt stayed for some time in Bhubaneshwar,Orissa in India to study Hindu religion and has drawn some material from this study. The Fishnet Hypothesis (2021) The Fishnet Hypothesis: Directed by Elizabeth Fields. With Elizabeth Fields, Trever Bowen. Miranda, a mermaid obsessed internet personality known for her quirky makeup tutorials, has placed an ad for a production assistant. Todd, the first of many to respond, has been chosen for an interview. Miranda films the interview process under the premise that she'd like her audience to connect with the ... assignment essay homework paper phd report resume review thesis