hypothesis scientific figure

• History & Society
• Science & Tech
• Biographies
• Animals & Nature
• Geography & Travel
• Arts & Culture
• Games & Quizzes
• On This Day
• One Good Fact
• New Articles
• Lifestyles & Social Issues
• Philosophy & Religion
• Politics, Law & Government
• World History
• Health & Medicine
• Browse Biographies
• Birds, Reptiles & Other Vertebrates
• Bugs, Mollusks & Other Invertebrates
• Environment
• Fossils & Geologic Time
• Entertainment & Pop Culture
• Sports & Recreation
• Visual Arts
• Demystified
• Image Galleries
• Infographics
• Top Questions
• Britannica Kids
• Saving Earth
• Space Next 50
• Student Center

• Where was science invented?
• When did science begin?

scientific hypothesis

Our editors will review what you’ve submitted and determine whether to revise the article.

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

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

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

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

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

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

• Privacy Policy

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

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

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

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

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

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

Null Hypothesis

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

Alternative Hypothesis

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

Directional Hypothesis

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

Non-directional Hypothesis

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

Statistical Hypothesis

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

Composite Hypothesis

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

Empirical Hypothesis

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

Simple Hypothesis

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

Complex Hypothesis

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

Applications of Hypothesis

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

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

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

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

Conduct a Literature Review

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

Determine the Variables

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

Formulate the Hypothesis

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

Write the Null Hypothesis

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

Refine the Hypothesis

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

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

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

Purpose of Hypothesis

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

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

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

When to use Hypothesis

Here are some common situations in which hypotheses are used:

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

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

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

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

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

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Research Methodology – Types, Examples and...

Background of The Study – Examples and Writing...

Evaluating Research – Process, Examples and...

Research Paper Conclusion – Writing Guide and...

Data Verification – Process, Types and Examples

APA Table of Contents – Format and Example

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• R Soc Open Sci
• v.10(8); 2023 Aug
• PMC10465209

On the scope of scientific hypotheses

William hedley thompson.

1 Department of Applied Information Technology, University of Gothenburg, Gothenburg, Sweden

2 Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

3 Department of Pedagogical, Curricular and Professional Studies, Faculty of Education, University of Gothenburg, Gothenburg, Sweden

4 Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden

Associated Data

This article has no additional data.

Hypotheses are frequently the starting point when undertaking the empirical portion of the scientific process. They state something that the scientific process will attempt to evaluate, corroborate, verify or falsify. Their purpose is to guide the types of data we collect, analyses we conduct, and inferences we would like to make. Over the last decade, metascience has advocated for hypotheses being in preregistrations or registered reports, but how to formulate these hypotheses has received less attention. Here, we argue that hypotheses can vary in specificity along at least three independent dimensions: the relationship, the variables, and the pipeline. Together, these dimensions form the scope of the hypothesis. We demonstrate how narrowing the scope of a hypothesis in any of these three ways reduces the hypothesis space and that this reduction is a type of novelty. Finally, we discuss how this formulation of hypotheses can guide researchers to formulate the appropriate scope for their hypotheses and should aim for neither too broad nor too narrow a scope. This framework can guide hypothesis-makers when formulating their hypotheses by helping clarify what is being tested, chaining results to previous known findings, and demarcating what is explicitly tested in the hypothesis.

1.  Introduction

Hypotheses are an important part of the scientific process. However, surprisingly little attention is given to hypothesis-making compared to other skills in the scientist's skillset within current discussions aimed at improving scientific practice. Perhaps this lack of emphasis is because the formulation of the hypothesis is often considered less relevant, as it is ultimately the scientific process that will eventually decide the veracity of the hypothesis. However, there are more hypotheses than scientific studies as selection occurs at various stages: from funder selection and researcher's interests. So which hypotheses are worthwhile to pursue? Which hypotheses are the most effective or pragmatic for extending or enhancing our collective knowledge? We consider the answer to these questions by discussing how broad or narrow a hypothesis can or should be (i.e. its scope).

We begin by considering that the two statements below are both hypotheses and vary in scope:

• H 1 : For every 1 mg decrease of x , y will increase by, on average, 2.5 points.
• H 2 : Changes in x 1 or x 2 correlate with y levels in some way.

Clearly, the specificity of the two hypotheses is very different. H 1 states a precise relationship between two variables ( x and y ), while H 2 specifies a vaguer relationship and does not specify which variables will show the relationship. However, they are both still hypotheses about how x and y relate to each other. This claim of various degrees of the broadness of hypotheses is, in and of itself, not novel. In Epistemetrics, Rescher [ 1 ], while drawing upon the physicist Duhem's work, develops what he calls Duhem's Law. This law considers a trade-off between certainty or precision in statements about physics when evaluating them. Duhem's Law states that narrower hypotheses, such as H 1 above, are more precise but less likely to be evaluated as true than broader ones, such as H 2 above. Similarly, Popper, when discussing theories, describes the reverse relationship between content and probability of a theory being true, i.e. with increased content, there is a decrease in probability and vice versa [ 2 ]. Here we will argue that it is important that both H 1 and H 2 are still valid scientific hypotheses, and their appropriateness depends on certain scientific questions.

The question of hypothesis scope is relevant since there are multiple recent prescriptions to improve science, ranging from topics about preregistrations [ 3 ], registered reports [ 4 ], open science [ 5 ], standardization [ 6 ], generalizability [ 7 ], multiverse analyses [ 8 ], dataset reuse [ 9 ] and general questionable research practices [ 10 ]. Within each of these issues, there are arguments to demarcate between confirmatory and exploratory research or normative prescriptions about how science should be done (e.g. science is ‘bad’ or ‘worse’ if code/data are not open). Despite all these discussions and improvements, much can still be done to improve hypothesis-making. A recent evaluation of preregistered studies in psychology found that over half excluded the preregistered hypotheses [ 11 ]. Further, evaluations of hypotheses in ecology showed that most hypotheses are not explicitly stated [ 12 , 13 ]. Other research has shown that obfuscated hypotheses are more prevalent in retracted research [ 14 ]. There have been recommendations for simpler hypotheses in psychology to avoid misinterpretations and misspecifications [ 15 ]. Finally, several evaluations of preregistration practices have found that a significant proportion of articles do not abide by their stated hypothesis or add additional hypotheses [ 11 , 16 – 18 ]. In sum, while multiple efforts exist to improve scientific practice, our hypothesis-making could improve.

One of our intentions is to provide hypothesis-makers with tools to assist them when making hypotheses. We consider this useful and timely as, with preregistrations becoming more frequent, the hypothesis-making process is now open and explicit . However, preregistrations are difficult to write [ 19 ], and preregistered articles can change or omit hypotheses [ 11 ] or they are vague and certain degrees of freedom hard to control for [ 16 – 18 ]. One suggestion has been to do less confirmatory research [ 7 , 20 ]. While we agree that all research does not need to be confirmatory, we also believe that not all preregistrations of confirmatory work must test narrow hypotheses. We think there is a possible point of confusion that the specificity in preregistrations, where researcher degrees of freedom should be stated, necessitates the requirement that the hypothesis be narrow. Our belief that this confusion is occurring is supported by the study Akker et al . [ 11 ] where they found that 18% of published psychology studies changed their preregistered hypothesis (e.g. its direction), and 60% of studies selectively reported hypotheses in some way. It is along these lines that we feel the framework below can be useful to help formulate appropriate hypotheses to mitigate these identified issues.

We consider this article to be a discussion of the researcher's different choices when formulating hypotheses and to help link hypotheses over time. Here we aim to deconstruct what aspects there are in the hypothesis about their specificity. Throughout this article, we intend to be neutral to many different philosophies of science relating to the scientific method (i.e. how one determines the veracity of a hypothesis). Our idea of neutrality here is that whether a researcher adheres to falsification, verification, pragmatism, or some other philosophy of science, then this framework can be used when formulating hypotheses. 1

The framework this article advocates for is that there are (at least) three dimensions that hypotheses vary along regarding their narrowness and broadness: the selection of relationships, variables, and pipelines. We believe this discussion is fruitful for the current debate regarding normative practices as some positions make, sometimes implicit, commitments about which set of hypotheses the scientific community ought to consider good or permissible. We proceed by outlining a working definition of ‘scientific hypothesis' and then discuss how it relates to theory. Then, we justify how hypotheses can vary along the three dimensions. Using this framework, we then discuss the scopes in relation to appropriate hypothesis-making and an argument about what constitutes a scientifically novel hypothesis. We end the article with practical advice for researchers who wish to use this framework.

2.  The scientific hypothesis

In this section, we will describe a functional and descriptive role regarding how scientists use hypotheses. Jeong & Kwon [ 21 ] investigated and summarized the different uses the concept of ‘hypothesis’ had in philosophical and scientific texts. They identified five meanings: assumption, tentative explanation, tentative cause, tentative law, and prediction. Jeong & Kwon [ 21 ] further found that researchers in science and philosophy used all the different definitions of hypotheses, although there was some variance in frequency between fields. Here we see, descriptively , that the way researchers use the word ‘hypothesis’ is diverse and has a wide range in specificity and function. However, whichever meaning a hypothesis has, it aims to be true, adequate, accurate or useful in some way.

Not all hypotheses are ‘scientific hypotheses'. For example, consider the detective trying to solve a crime and hypothesizing about the perpetrator. Such a hypothesis still aims to be true and is a tentative explanation but differs from the scientific hypothesis. The difference is that the researcher, unlike the detective, evaluates the hypothesis with the scientific method and submits the work for evaluation by the scientific community. Thus a scientific hypothesis entails a commitment to evaluate the statement with the scientific process . 2 Additionally, other types of hypotheses can exist. As discussed in more detail below, scientific theories generate not only scientific hypotheses but also contain auxiliary hypotheses. The latter refers to additional assumptions considered to be true and not explicitly evaluated. 3

Next, the scientific hypothesis is generally made antecedent to the evaluation. This does not necessitate that the event (e.g. in archaeology) or the data collection (e.g. with open data reuse) must be collected before the hypothesis is made, but that the evaluation of the hypothesis cannot happen before its formulation. This claim state does deny the utility of exploratory hypothesis testing of post hoc hypotheses (see [ 25 ]). However, previous results and exploration can generate new hypotheses (e.g. via abduction [ 22 , 26 – 28 ], which is the process of creating hypotheses from evidence), which is an important part of science [ 29 – 32 ], but crucially, while these hypotheses are important and can be the conclusion of exploratory work, they have yet to be evaluated (by whichever method of choice). Hence, they still conform to the antecedency requirement. A further way to justify the antecedency is seen in the practice of formulating a post hoc hypothesis, and considering it to have been evaluated is seen as a questionable research practice (known as ‘hypotheses after results are known’ or HARKing [ 33 ]). 4

While there is a varying range of specificity, is the hypothesis a critical part of all scientific work, or is it reserved for some subset of investigations? There are different opinions regarding this. Glass and Hall, for example, argue that the term only refers to falsifiable research, and model-based research uses verification [ 36 ]. However, this opinion does not appear to be the consensus. Osimo and Rumiati argue that any model based on or using data is never wholly free from hypotheses, as hypotheses can, even implicitly, infiltrate the data collection [ 37 ]. For our definition, we will consider hypotheses that can be involved in different forms of scientific evaluation (i.e. not just falsification), but we do not exclude the possibility of hypothesis-free scientific work.

Finally, there is a debate about whether theories or hypotheses should be linguistic or formal [ 38 – 40 ]. Neither side in this debate argues that verbal or formal hypotheses are not possible, but instead, they discuss normative practices. Thus, for our definition, both linguistic and formal hypotheses are considered viable.

Considering the above discussion, let us summarize the scientific process and the scientific hypothesis: a hypothesis guides what type of data are sampled and what analysis will be done. With the new observations, evidence is analysed or quantified in some way (often using inferential statistics) to judge the hypothesis's truth value, utility, credibility, or likelihood. The following working definition captures the above:

• Scientific hypothesis : an implicit or explicit statement that can be verbal or formal. The hypothesis makes a statement about some natural phenomena (via an assumption, explanation, cause, law or prediction). The scientific hypothesis is made antecedent to performing a scientific process where there is a commitment to evaluate it.

For simplicity, we will only use the term ‘hypothesis’ for ‘scientific hypothesis' to refer to the above definition for the rest of the article except when it is necessary to distinguish between other types of hypotheses. Finally, this definition could further be restrained in multiple ways (e.g. only explicit hypotheses are allowed, or assumptions are never hypotheses). However, if the definition is more (or less) restrictive, it has little implication for the argument below.

3.  The hypothesis, theory and auxiliary assumptions

While we have a definition of the scientific hypothesis, we have yet to link it with how it relates to scientific theory, where there is frequently some interconnection (i.e. a hypothesis tests a scientific theory). Generally, for this paper, we believe our argument applies regardless of how scientific theory is defined. Further, some research lacks theory, sometimes called convenience or atheoretical studies [ 41 ]. Here a hypothesis can be made without a wider theory—and our framework fits here too. However, since many consider hypotheses to be defined or deducible from scientific theory, there is an important connection between the two. Therefore, we will briefly clarify how hypotheses relate to common formulations of scientific theory.

A scientific theory is generally a set of axioms or statements about some objects, properties and their relations relating to some phenomena. Hypotheses can often be deduced from the theory. Additionally, a theory has boundary conditions. The boundary conditions specify the domain of the theory stating under what conditions it applies (e.g. all things with a central neural system, humans, women, university teachers) [ 42 ]. Boundary conditions of a theory will consequently limit all hypotheses deduced from the theory. For example, with a boundary condition ‘applies to all humans’, then the subsequent hypotheses deduced from the theory are limited to being about humans. While this limitation of the hypothesis by the theory's boundary condition exists, all the considerations about a hypothesis scope detailed below still apply within the boundary conditions. Finally, it is also possible (depending on the definition of scientific theory) for a hypothesis to test the same theory under different boundary conditions. 5

The final consideration relating scientific theory to scientific hypotheses is auxiliary hypotheses. These hypotheses are theories or assumptions that are considered true simultaneously with the theory. Most philosophies of science from Popper's background knowledge [ 24 ], Kuhn's paradigms during normal science [ 44 ], and Laktos' protective belt [ 45 ] all have their own versions of this auxiliary or background information that is required for the hypothesis to test the theory. For example, Meelh [ 46 ] auxiliary theories/assumptions are needed to go from theoretical terms to empirical terms (e.g. neural activity can be inferred from blood oxygenation in fMRI research or reaction time to an indicator of cognition) and auxiliary theories about instruments (e.g. the experimental apparatus works as intended) and more (see also Other approaches to categorizing hypotheses below). As noted in the previous section, there is a difference between these auxiliary hypotheses, regardless of their definition, and the scientific hypothesis defined above. Recall that our definition of the scientific hypothesis included a commitment to evaluate it. There are no such commitments with auxiliary hypotheses, but rather they are assumed to be correct to test the theory adequately. This distinction proves to be important as auxiliary hypotheses are still part of testing a theory but are separate from the hypothesis to be evaluated (discussed in more detail below).

4.  The scope of hypotheses

In the scientific hypothesis section, we defined the hypothesis and discussed how it relates back to the theory. In this section, we want to defend two claims about hypotheses:

• (A1) Hypotheses can have different scopes . Some hypotheses are narrower in their formulation, and some are broader.
• (A2) The scope of hypotheses can vary along three dimensions relating to relationship selection , variable selection , and pipeline selection .

A1 may seem obvious, but it is important to establish what is meant by narrower and broader scope. When a hypothesis is very narrow, it is specific. For example, it might be specific about the type of relationship between some variables. In figure 1 , we make four different statements regarding the relationship between x and y . The narrowest hypothesis here states ‘there is a positive linear relationship with a magnitude of 0.5 between x and y ’ ( figure 1 a ), and the broadest hypothesis states ‘there is a relationship between x and y ’ ( figure 1 d ). Note that many other hypotheses are possible that are not included in this example (such as there being no relationship).

Examples of narrow and broad hypotheses between x and y . Circles indicate a set of possible relationships with varying slopes that can pivot or bend.

We see that the narrowest of these hypotheses claims a type of relationship (linear), a direction of the relationship (positive) and a magnitude of the relationship (0.5). As the hypothesis becomes broader, the specific magnitude disappears ( figure 1 b ), the relationship has additional options than just being linear ( figure 1 c ), and finally, the direction of the relationship disappears. Crucially, all the examples in figure 1 can meet the above definition of scientific hypotheses. They are all statements that can be evaluated with the same scientific method. There is a difference between these statements, though— they differ in the scope of the hypothesis . Here we have justified A1.

Within this framework, when we discuss whether a hypothesis is narrower or broader in scope, this is a relation between two hypotheses where one is a subset of the other. This means that if H 1 is narrower than H 2 , and if H 1 is true, then H 2 is also true. This can be seen in figure 1 a–d . Suppose figure 1 a , the narrowest of all the hypotheses, is true. In that case, all the other broader statements are also true (i.e. a linear correlation of 0.5 necessarily entails that there is also a positive linear correlation, a linear correlation, and some relationship). While this property may appear trivial, it entails that it is only possible to directly compare the hypothesis scope between two hypotheses (i.e. their broadness or narrowness) where one is the subset of the other. 6

4.1. Sets, disjunctions and conjunctions of elements

The above restraint defines the scope as relations between sets. This property helps formalize the framework of this article. Below, when we discuss the different dimensions that can impact the scope, these become represented as a set. Each set contains elements. Each element is a permissible situation that allows the hypothesis to be accepted. We denote elements as lower case with italics (e.g. e 1 , e 2 , e 3 ) and sets as bold upper case (e.g. S ). Each of the three different dimensions discussed below will be formalized as sets, while the total number of elements specifies their scope.

Let us reconsider the above restraint about comparing hypotheses as narrower or broader. This can be formally shown if:

• e 1 , e 2 , e 3 are elements of S 1 ; and
• e 1 and e 2 are elements of S 2 ,

then S 2 is narrower than S 1 .

Each element represents specific propositions that, if corroborated, would support the hypothesis. Returning to figure 1 a , b , the following statements apply to both:

• ‘There is a positive linear relationship between x and y with a slope of 0.5’.

Whereas the following two apply to figure 1 b but not figure 1 a :

• ‘There is a positive linear relationship between x and y with a slope of 0.4’ ( figure 1 b ).
• ‘There is a positive linear relationship between x and y with a slope of 0.3’ ( figure 1 b ).

Figure 1 b allows for a considerably larger number of permissible situations (which is obvious as it allows for any positive linear relationship). When formulating the hypothesis in figure 1 b , we do not need to specify every single one of these permissible relationships. We can simply specify all possible positive slopes, which entails the set of permissible elements it includes.

That broader hypotheses have more elements in their sets entails some important properties. When we say S contains the elements e 1 , e 2 , and e 3 , the hypothesis is corroborated if e 1 or e 2 or e 3 is the case. This means that the set requires only one of the elements to be corroborated for the hypothesis to be considered correct (i.e. the positive linear relationship needs to be 0.3 or 0.4 or 0.5). Contrastingly, we will later see cases when conjunctions of elements occur (i.e. both e 1 and e 2 are the case). When a conjunction occurs, in this formulation, the conjunction itself becomes an element in the set (i.e. ‘ e 1 and e 2 ’ is a single element). Figure 2 illustrates how ‘ e 1 and e 2 ’ is narrower than ‘ e 1 ’, and ‘ e 1 ’ is narrower than ‘ e 1 or e 2 ’. 7 This property relating to the conjunction being narrower than individual elements is explained in more detail in the pipeline selection section below.

Scope as sets. Left : four different sets (grey, red, blue and purple) showing different elements which they contain. Right : a list of each colour explaining which set is a subset of the other (thereby being ‘narrower’).

4.2. Relationship selection

We move to A2, which is to show the different dimensions that a hypothesis scope can vary along. We have already seen an example of the first dimension of a hypothesis in figure 1 , the relationship selection . Let R denote the set of all possible configurations of relationships that are permissible for the hypothesis to be considered true. For example, in the narrowest formulation above, there was one allowed relationship for the hypothesis to be true. Consequently, the size of R (denoted | R |) is one. As discussed above, in the second narrowest formulation ( figure 1 b ), R has more possible relationships where it can still be considered true:

• r 1 = ‘a positive linear relationship of 0.1’
• r 2 = ‘a positive linear relationship of 0.2’
• r 3 = ‘a positive linear relationship of 0.3’.

Additionally, even broader hypotheses will be compatible with more types of relationships. In figure 1 c , d , nonlinear and negative relationships are also possible relationships included in R . For this broader statement to be affirmed, more elements are possible to be true. Thus if | R | is greater (i.e. contains more possible configurations for the hypothesis to be true), then the hypothesis is broader. Thus, the scope of relating to the relationship selection is specified by | R |. Finally, if |R H1 | > |R H2 | , then H 1 is broader than H 2 regarding the relationship selection.

Figure 1 is an example of the relationship narrowing. That the relationship became linear is only an example and does not necessitate a linear relationship or that this scope refers only to correlations. An alternative example of a relationship scope is a broad hypothesis where there is no knowledge about the distribution of some data. In such situations, one may assume a uniform relationship or a Cauchy distribution centred at zero. Over time the specific distribution can be hypothesized. Thereafter, the various parameters of the distribution can be hypothesized. At each step, the hypothesis of the distribution gets further specified to narrower formulations where a smaller set of possible relationships are included (see [ 47 , 48 ] for a more in-depth discussion about how specific priors relate to more narrow tests). Finally, while figure 1 was used to illustrate the point of increasingly narrow relationship hypotheses, it is more likely to expect the narrowest relationship, within fields such as psychology, to have considerable uncertainty and be formulated with confidence or credible intervals (i.e. we will rarely reach point estimates).

4.3. Variable selection

We have demonstrated that relationship selection can affect the scope of a hypothesis. Additionally, at least two other dimensions can affect the scope of a hypothesis: variable selection and pipeline selection . The variable selection in figure 1 was a single bivariate relationship (e.g. x 's relationship with y ). However, it is not always the case that we know which variables will be involved. For example, in neuroimaging, we can be confident that one or more brain regions will be processing some information following a stimulus. Still, we might not be sure which brain region(s) this will be. Consequently, our hypothesis becomes broader because we have selected more variables. The relationship selection may be identical for each chosen variable, but the variable selection becomes broader. We can consider the following three hypotheses to be increasing in their scope:

• H 1 : x relates to y with relationship R .
• H 2 : x 1 or x 2 relates to y with relationship R .
• H 3 : x 1 or x 2 or x 3 relates to y with relationship R .

For H 1 –H 3 above, we assume that R is the same. Further, we assume that there is no interaction between these variables.

In the above examples, we have multiple x ( x 1 , x 2 , x 3 , … , x n ). Again, we can symbolize the variable selection as a non-empty set XY , containing either a single variable or many variables. Our motivation for designating it XY is that the variable selection can include multiple possibilities for both the independent variable ( x ) and the dependent variable ( y ). Like with relationship selection, we can quantify the broadness between two hypotheses with the size of the set XY . Consequently, | XY | denotes the total scope concerning variable selection. Thus, in the examples above | XY H1 | < | XY H2 | < | XY H3 |. Like with relationship selection, hypotheses that vary in | XY | still meet the definition of a hypothesis. 8

An obvious concern for many is that a broader XY is much easier to evaluate as correct. Generally, when | XY 1 | > | XY 2 |, there is a greater chance of spurious correlations when evaluating XY 1 . This concern is an issue relating to the evaluation of hypotheses (e.g. applying statistics to the evaluation), which will require additional assumptions relating to how to evaluate the hypotheses. Strategies to deal with this apply some correction or penalization for multiple statistical testing [ 49 ] or partial pooling and regularizing priors [ 50 , 51 ]. These strategies aim to evaluate a broader variable selection ( x 1 or x 2 ) on equal or similar terms to a narrow variable selection ( x 1 ).

4.4. Pipeline selection

Scientific studies require decisions about how to perform the analysis. This scope considers transformations applied to the raw data ( XY raw ) to achieve some derivative ( XY ). These decisions can also involve selection procedures that drop observations deemed unreliable, standardizing, correcting confounding variables, or different philosophies. We can call the array of decisions and transformations used as the pipeline . A hypothesis varies in the number of pipelines:

• H 1 : XY has a relationship(s) R with pipeline p 1 .
• H 2 : XY has a relationship(s) R with pipeline p 1 or pipeline p 2 .
• H 3 : XY has a relationship(s) R with pipeline p 1 or pipeline p 2 , or pipeline p 3 .

Importantly, the pipeline here considers decisions regarding how the hypothesis shapes the data collection and transformation. We do not consider this to include decisions made regarding the assumptions relating to the statistical inference as those relate to operationalizing the evaluation of the hypothesis and not part of the hypothesis being evaluated (these assumptions are like auxiliary hypotheses, which are assumed to be true but not explicitly evaluated).

Like with variable selection ( XY ) and relationship selection ( R ), we can see that pipelines impact the scope of hypotheses. Again, we can symbolize the pipeline selection with a set P . As previously, | P | will denote the dimension of the pipeline selection. In the case of pipeline selection, we are testing the same variables, looking for the same relationship, but processing the variables or relationships with different pipelines to evaluate the relationship. Consequently, | P H1 | < | P H2 | < | P H3 |.

These issues regarding pipelines have received attention as the ‘garden of forking paths' [ 52 ]. Here, there are calls for researchers to ensure that their entire pipeline has been specified. Additionally, recent work has highlighted the diversity of results based on multiple analytical pipelines [ 53 , 54 ]. These results are often considered a concern, leading to calls that results should be pipeline resistant.

The wish for pipeline-resistant methods entails that hypotheses, in their narrowest form, are possible for all pipelines. Consequently, a narrower formulation will entail that this should not impact the hypothesis regardless of which pipeline is chosen. Thus the conjunction of pipelines is narrower than single pipelines. Consider the following three scenarios:

• H 3 : XY has a relationship(s) R with pipeline p 1 and pipeline p 2 .

In this instance, since H 1 is always true if H 3 is true, thus H 3 is a narrower formulation than H 1 . Consequently, | P H3 | < | P H1 | < | P H2 |. Decreasing the scope of the pipeline dimension also entails the increase in conjunction of pipelines (i.e. creating pipeline-resistant methods) rather than just the reduction of disjunctional statements.

4.5. Combining the dimensions

In summary, we then have three different dimensions that independently affect the scope of the hypothesis. We have demonstrated the following general claim regarding hypotheses:

• The variables XY have a relationship R with pipeline P .

And that the broadness and narrowness of a hypothesis depend on how large the three sets XY , R and P are. With this formulation, we can conclude that hypotheses have a scope that can be determined with a 3-tuple argument of (| R |, | XY |, | P |).

While hypotheses can be formulated along these three dimensions and generally aim to be reduced, it does not entail that these dimensions behave identically. For example, the relationship dimensions aim to reduce the number of elements as far as possible (e.g. to an interval). Contrastingly, for both variables and pipeline, the narrower hypothesis can reduce to single variables/pipelines or become narrower still and become conjunctions where all variables/pipelines need to corroborate the hypothesis (i.e. regardless of which method one follows, the hypothesis is correct).

5.  Additional possible dimensions

No commitment is being made about the exhaustive nature of there only being three dimensions that specify the hypothesis scope. Other dimensions may exist that specify the scope of a hypothesis. For example, one might consider the pipeline dimension as two different dimensions. The first would consider the experimental pipeline dimension regarding all variables relating to the experimental setup to collect data, and the latter would be the analytical pipeline dimension regarding the data analysis of any given data snapshot. Another possible dimension is adding the number of situations or contexts under which the hypothesis is valid. For example, any restraint such as ‘in a vacuum’, ‘under the speed of light’, or ‘in healthy human adults' could be considered an additional dimension of the hypothesis. There is no objection to whether these should be additional dimensions of the hypothesis. However, as stated above, these usually follow from the boundary conditions of the theory.

6.  Specifying the scope versus assumptions

We envision that this framework can help hypothesis-makers formulate hypotheses (in research plans, registered reports, preregistrations etc.). Further, using this framework while formulating hypotheses can help distinguish between auxiliary hypotheses and parts of the scientific hypothesis being tested. When writing preregistrations, it can frequently occur that some step in the method has two alternatives (e.g. a preprocessing step), and there is not yet reason to choose one over the other, and the researcher needs to make a decision. These following scenarios are possible:

• 1. Narrow pipeline scope . The researcher evaluates the hypothesis with both pipeline variables (i.e. H holds for both p 1 and p 2 where p 1 and p 2 can be substituted with each other in the pipeline).
• 2. Broad pipeline scope. The researcher evaluates the hypothesis with both pipeline variables, and only one needs to be correct (i.e. H holds for either p 1 or p 2 where p 1 and p 2 can be substituted with each other in the pipeline). The result of this experiment may help motivate choosing either p 1 or p 2 in future studies.
• 3. Auxiliary hypothesis. Based on some reason (e.g. convention), the researcher assumes p 1 and evaluates H assuming p 1 is true.

Here we see that the same pipeline step can be part of either the auxiliary hypotheses or the pipeline scope. This distinction is important because if (3) is chosen, the decision becomes an assumption that is not explicitly tested by the hypothesis. Consequently, a researcher confident in the hypothesis may state that the auxiliary hypothesis p 1 was incorrect, and they should retest their hypothesis using different assumptions. In the cases where this decision is part of the pipeline scope, the hypothesis is intertwined with this decision, removing the eventual wiggle-room to reject auxiliary hypotheses that were assumed. Furthermore, starting with broader pipeline hypotheses that gradually narrow down can lead to a more well-motivated protocol for approaching the problem. Thus, this framework can help researchers while writing their hypotheses in, for example, preregistrations because they can consider when they are committing to a decision, assuming it, or when they should perhaps test a broader hypothesis with multiple possible options (discussed in more detail in §11 below).

7.  The reduction of scope in hypothesis space

Having established that different scopes of a hypothesis are possible, we now consider how the hypotheses change over time. In this section, we consider how the scope of the hypothesis develops ideally within science.

Consider a new research question. A large number of hypotheses are possible. Let us call this set of all possible hypotheses the hypothesis space . Hypotheses formulated within this space can be narrower or broader based on the dimensions discussed previously ( figure 3 ).

Example of hypothesis space. The hypothesis scope is expressed as cuboids in three dimensions (relationship ( R ), variable ( XY ), pipeline ( P )). The hypothesis space is the entire possible space within the three dimensions. Three hypotheses are shown in the hypothesis space (H 1 , H 2 , H 3 ). H 2 and H 3 are subsets of H 1 .

After the evaluation of the hypothesis with the scientific process, the hypothesis will be accepted or rejected. 9 The evaluation could be done through falsification or via verification, depending on the philosophy of science commitments. Thereafter, other narrower formulations of the hypothesis can be formulated by reducing the relationship, variable or pipeline scope. If a narrower hypothesis is accepted, more specific details about the subject matter are known, or a theory has been refined in greater detail. A narrower hypothesis will entail a more specific relationship, variable or pipeline detailed in the hypothesis. Consequently, hypotheses linked to each other in this way will become narrower over time along one or more dimensions. Importantly, considering that the conjunction of elements is narrower than single elements for pipelines and variables, this process of narrower hypotheses will lead to more general hypotheses (i.e. they have to be applied in all conditions and yield less flexibility when they do not apply). 10

Considering that the scopes of hypotheses were defined as sets above, some properties can be deduced from this framework about how narrower hypotheses relate to broader hypotheses. Let us consider three hypotheses (H 1 , H 2 , and H 3 ; figure 3 ). H 2 and H 3 are non-overlapping subsets of H 1 . Thus H 2 and H 3 are both narrower in scope than H 1 . Thus the following is correct:

• P1: If H 1 is false, then H 2 is false, and H 2 does not need to be evaluated.
• P2: If H 2 is true, then the broader H 1 is true, and H 1 does not need to be evaluated.
• P3: If H 1 is true and H 2 is false, some other hypothesis H 3 of similar scope to H 2 is possible.

For example, suppose H 1 is ‘there is a relationship between x and y ’, H 2 is ‘there is a positive relationship between x and y ’, and H 3 is ‘a negative relationship between x and y ’. In that case, it becomes apparent how each of these follows. 11 Logically, many deductions from set theory are possible but will not be explored here. Instead, we will discuss two additional consequences of hypothesis scopes: scientific novelty and applications for the researcher who formulates a hypothesis.

P1–P3 have been formulated as hypotheses being true or false. In practice, hypotheses are likely evaluated probabilistically (e.g. ‘H 1 is likely’ or ‘there is evidence in support of H 1 ’). In these cases, P1–P3 can be rephrased to account for this by substituting true/false with statements relating to evidence. For example, P2 could read: ‘If there is evidence in support of H 2 , then there is evidence in support of H 1 , and H 1 does not need to be evaluated’.

8.  Scientific novelty as the reduction of scope

Novelty is a key concept that repeatedly occurs in multiple aspects of the scientific enterprise, from funding to publishing [ 55 ]. Generally, scientific progress establishes novel results based on some new hypothesis. Consequently, the new hypothesis for the novel results must be narrower than previously established knowledge (i.e. the size of the scopes is reduced). Otherwise, the result is trivial and already known (see P2 above). Thus, scientific work is novel if the scientific process produces a result based on hypotheses with either a smaller | R |, | XY |, or | P | compared to previous work.

This framework of dimensions of the scope of a hypothesis helps to demarcate when a hypothesis and the subsequent result are novel. If previous studies have established evidence for R 1 (e.g. there is a positive relationship between x and y ), a hypothesis will be novel if and only if it is narrower than R 1 . Thus, if R 2 is narrower in scope than R 1 (i.e. | R 2 | < | R 1 |), R 2 is a novel hypothesis.

Consider the following example. Study 1 hypothesizes, ‘There is a positive relationship between x and y ’. It identifies a linear relationship of 0.6. Next, Study 2 hypothesizes, ‘There is a specific linear relationship between x and y that is 0.6’. Study 2 also identifies the relationship of 0.6. Since this was a narrower hypothesis, Study 2 is novel despite the same result. Frequently, researchers claim that they are the first to demonstrate a relationship. Being the first to demonstrate a relationship is not the final measure of novelty. Having a narrower hypothesis than previous researchers is a sign of novelty as it further reduces the hypothesis space.

Finally, it should be noted that novelty is not the only objective of scientific work. Other attributes, such as improving the certainty of a current hypothesis (e.g. through replications), should not be overlooked. Additional scientific explanations and improved theories are other aspects. Additionally, this definition of novelty relating to hypothesis scope does not exclude other types of novelty (e.g. new theories or paradigms).

9.  How broad should a hypothesis be?

Given the previous section, it is elusive to conclude that the hypothesis should be as narrow as possible as it entails maximal knowledge gain and scientific novelty when formulating hypotheses. Indeed, many who advocate for daring or risky tests seem to hold this opinion. For example, Meehl [ 46 ] argues that we should evaluate theories based on point (or interval) prediction, which would be compatible with very narrow versions of relationships. We do not necessarily think that this is the most fruitful approach. In this section, we argue that hypotheses should aim to be narrower than current knowledge , but too narrow may be problematic .

Let us consider the idea of confirmatory analyses. These studies will frequently keep the previous hypothesis scopes regarding P and XY but aim to become more specific regarding R (i.e. using the same method and the same variables to detect a more specific relationship). A very daring or narrow hypothesis is to minimize R to include the fewest possible relationships. However, it becomes apparent that simply pursuing specificness or daringness is insufficient for selecting relevant hypotheses. Consider a hypothetical scenario where a researcher believes virtual reality use leads people to overestimate the amount of exercise they have done. If unaware of previous studies on this project, an apt hypothesis is perhaps ‘increased virtual reality usage correlates with a less accuracy of reported exercise performed’ (i.e. R is broad). However, a more specific and more daring hypothesis would be to specify the relationship further. Thus, despite not knowing if there is a relationship at all, a more daring hypothesis could be: ‘for every 1 h of virtual reality usage, there will be, on average, a 0.5% decrease in the accuracy of reported exercise performed’ (i.e. R is narrow). We believe it would be better to establish the broader hypothesis in any scenario here for the first experiment. Otherwise, if we fail to confirm the more specific formulation, we could reformulate another equally narrow relative to the broader hypothesis. This process of tweaking a daring hypothesis could be pursued ad infinitum . Such a situation will neither quickly identify the true hypothesis nor effectively use limited research resources.

By first discounting a broader hypothesis that there is no relationship, it will automatically discard all more specific formulations of that relationship in the hypothesis space. Returning to figure 3 , it will be better to establish H 1 before attempting H 2 or H 3 to ensure the correct area in the hypothesis space is being investigated. To provide an analogy: when looking for a needle among hay, first identify which farm it is at, then which barn, then which haystack, then which part of the haystack it is at before we start picking up individual pieces of hay. Thus, it is preferable for both pragmatic and cost-of-resource reasons to formulate sufficiently broad hypotheses to navigate the hypothesis space effectively.

Conversely, formulating too broad a relationship scope in a hypothesis when we already have evidence for narrower scope would be superfluous research (unless the evidence has been called into question by, for example, not being replicated). If multiple studies have supported the hypothesis ‘there is a 20-fold decrease in mortality after taking some medication M’, it would be unnecessary to ask, ‘Does M have any effect?’.

Our conclusion is that the appropriate scope of a hypothesis, and its three dimensions, follow a Goldilocks-like principle where too broad is superfluous and not novel, while too narrow is unnecessary or wasteful. Considering the scope of one's hypothesis and how it relates to previous hypotheses' scopes ensures one is asking appropriate questions.

Finally, there has been a recent trend in psychology that hypotheses should be formal [ 38 , 56 – 60 ]. Formal theories are precise since they are mathematical formulations entailing that their interpretations are clear (non-ambiguous) compared to linguistic theories. However, this literature on formal theories often refers to ‘precise predictions’ and ‘risky testing’ while frequently referencing Meehl, who advocates for narrow hypotheses (e.g. [ 38 , 56 , 59 ]). While perhaps not intended by any of the proponents, one interpretation of some of these positions is that hypotheses derived from formal theories will be narrow hypotheses (i.e. the quality of being ‘precise’ can mean narrow hypotheses with risky tests and non-ambiguous interpretations simultaneously). However, the benefit from the clarity (non-ambiguity) that formal theories/hypotheses bring also applies to broad formal hypotheses as well. They can include explicit but formalized versions of uncertain relationships, multiple possible pipelines, and large sets of variables. For example, a broad formal hypothesis can contain a hyperparameter that controls which distribution the data fit (broad relationship scope), or a variable could represent a set of formalized explicit pipelines (broad pipeline scope) that will be tested. In each of these instances, it is possible to formalize non-ambiguous broad hypotheses from broad formal theories that do not yet have any justification for being overly narrow. In sum, our argumentation here stating that hypotheses should not be too narrow is not an argument against formal theories but rather that hypotheses (derived from formal theories) do not necessarily have to be narrow.

10.  Other approaches to categorizing hypotheses

The framework we present here is a way of categorizing hypotheses into (at least) three dimensions regarding the hypothesis scope, which we believe is accessible to researchers and help link scientific work over time while also trying to remain neutral with regard to a specific philosophy of science. Our proposal does not aim to be antagonistic or necessarily contradict other categorizing schemes—but we believe that our framework provides benefits.

One recent categorization scheme is the Theoretical (T), Auxiliary (A), Statistical (S) and Inferential (I) assumption model (together becoming the TASI model) [ 61 , 62 ]. Briefly, this model considers theory to generate theoretical hypotheses. To translate from theoretical unobservable terms (e.g. personality, anxiety, mass), auxiliary assumptions are needed to generate an empirical hypothesis. Statistical assumptions are often needed to test the empirical hypothesis (e.g. what is the distribution, is it skewed or not) [ 61 , 62 ]. Finally, additional inferential assumptions are needed to generalize to a larger population (e.g. was there a random and independent sampling from defined populations). The TASI model is insightful and helpful in highlighting the distance between a theory and the observation that would corroborate/contradict it. Part of its utility is to bring auxiliary hypotheses into the foreground, to improve comparisons between studies and improve theory-based interventions [ 63 , 64 ].

We do agree with the importance of being aware of or stating the auxiliary hypotheses, but there are some differences between the frameworks. First, the number of auxiliary assumptions in TASI can be several hundred [ 62 ], whereas our framework will consider some of them as part of the pipeline dimension. Consider the following four assumptions: ‘the inter-stimulus interval is between 2000 ms and 3000 ms', ‘the data will be z-transformed’, ‘subjects will perform correctly’, and ‘the measurements were valid’. According to the TASI model, all these will be classified similarly as auxiliary assumptions. Contrarily, within our framework, it is possible to consider the first two as part of the pipeline dimension and the latter two as auxiliary assumptions, and consequently, the first two become integrated as part of the hypothesis being tested and the latter two auxiliary assumptions. A second difference between the frameworks relates to non-theoretical studies (convenience, applied or atheoretical). Our framework allows for the possibility that the hypothesis space generated by theoretical and convenience studies can interact and inform each other within the same framework . Contrarily, in TASI, the theory assumptions no longer apply, and a different type of hypothesis model is needed; these assumptions must be replaced by another group of assumptions (where ‘substantive application assumptions' replace the T and the A, becoming SSI) [ 61 ]. Finally, part of our rationale for our framework is to be able to link and track hypotheses and hypothesis development together over time, so our classification scheme has different utility.

Another approach which has some similar utility to this framework is theory construction methodology (TCM) [ 57 ]. The similarity here is that TCM aims to be a practical guide to improve theory-making in psychology. It is an iterative process which relates theory, phenomena and data. Here hypotheses are not an explicit part of the model. However, what is designated as ‘proto theory’ could be considered a hypothesis in our framework as they are a product of abduction, shaping the theory space. Alternatively, what is deduced to evaluate the theory can also be considered a hypothesis. We consider both possible and that our framework can integrate with these two steps, especially since TCM does not have clear guidelines for how to do each step.

11.  From theory to practice: implementing this framework

We believe that many practising researchers can relate to many aspects of this framework. But, how can a researcher translate the above theoretical framework to their work? The utility of this framework lies in bringing these three scopes of a hypothesis together and explaining how each can be reduced. We believe researchers can use this framework to describe their current practices more clearly. Here we discuss how it can be helpful for researchers when formulating, planning, preregistering, and discussing the evaluation of their scientific hypotheses. These practical implications are brief, and future work can expand on the connection between the full interaction between hypothesis space and scope. Furthermore, both authors have the most experience in cognitive neuroscience, and some of the practical implications may revolve around this type of research and may not apply equally to other fields.

11.1. Helping to form hypotheses

Abduction, according to Peirce, is a hypothesis-making exercise [ 22 , 26 – 28 ]. Given some observations, a general testable explanation of the phenomena is formed. However, when making the hypothesis, this statement will have a scope (either explicitly or implicitly). Using our framework, the scope can become explicit. The hypothesis-maker can start with ‘The variables XY have a relationship R with pipeline P ’ as a scaffold to form the hypothesis. From here, the hypothesis-maker can ‘fill in the blanks’, explicitly adding each of the scopes. Thus, when making a hypothesis via abduction and using our framework, the hypothesis will have an explicit scope when it is made. By doing this, there is less chance that a formulated hypothesis is unclear, ambiguous, and needs amending at a later stage.

11.2. Assisting to clearly state hypotheses

A hypothesis is not just formulated but also communicated. Hypotheses are stated in funding applications, preregistrations, registered reports, and academic articles. Further, preregistered hypotheses are often omitted or changed in the final article [ 11 ], and hypotheses are not always explicitly stated in articles [ 12 ]. How can this framework help to make better hypotheses? Similar to the previous point, filling in the details of ‘The variables XY have a relationship R with pipeline P ’ is an explicit way to communicate the hypothesis. Thinking about each of these dimensions should entail an appropriate explicit scope and, hopefully, less variation between preregistered and reported hypotheses. The hypothesis does not need to be a single sentence, and details of XY and P will often be developed in the methods section of the text. However, using this template as a starting point can help ensure the hypothesis is stated, and the scope of all three dimensions has been communicated.

11.3. Helping to promote explicit and broad hypotheses instead of vague hypotheses

There is an important distinction between vague hypotheses and broad hypotheses, and this framework can help demarcate between them. A vague statement would be: ‘We will quantify depression in patients after treatment’. Here there is uncertainty relating to how the researcher will go about doing the experiment (i.e. how will depression be quantified?). However, a broad statement can be uncertain, but the uncertainty is part of the hypothesis: ‘Two different mood scales (S 1 or S 2 ) will be given to patients and test if only one (or both) changed after treatment’. This latter statement is transparently saying ‘S 1 or S 2 ’ is part of a broad hypothesis—the uncertainty is whether the two different scales are quantifying the same construct. We keep this uncertainty within the broad hypothesis, which will get evaluated, whereas a vague hypothesis has uncertainty as part of the interpretation of the hypothesis. This framework can be used when formulating hypotheses to help be broad (where needed) but not vague.

11.4. Which hypothesis should be chosen?

When considering the appropriate scope above, we argued for a Goldilocks-like principle of determining the hypothesis that is not too broad or too narrow. However, when writing, for example, a preregistration, how does one identify this sweet spot? There is no easy or definite universal answer to this question. However, one possible way is first to identify the XY , R , and P of previous hypotheses. From here, identify what a non-trivial step is to improve our knowledge of the research area. So, for example, could you be more specific about the exact nature of the relationship between the variables? Does the pipeline correspond to today's scientific standards, or were some suboptimal decisions made? Is there another population that you think the previous result also applies to? Do you think that maybe a more specific construct or subpopulation might explain the previous result? Could slightly different constructs (perhaps easier to quantify) be used to obtain a similar relationship? Are there even more constructs to which this relationship should apply simultaneously? Are you certain of the direction of the relationship? Answering affirmatively to any of these questions will likely make a hypothesis narrower and connect to previous research while being clear and explicit. Moreover, depending on the research question, answering any of these may be sufficiently narrow to be a non-trivial innovation. However, there are many other ways to make a hypothesis narrower than these guiding questions.

11.5. The confirmatory–exploratory continuum

Research is often dichotomized into confirmatory (testing a hypothesis) or exploratory (without a priori hypotheses). With this framework, researchers can consider how their research acts on some hypothesis space. Confirmatory and exploratory work has been defined in terms of how each interacts with the researcher's degrees of freedom (where confirmatory aims to reduce while exploratory utilizes them [ 30 ]). Both broad confirmatory and narrow exploratory research are possible using this definition and possible within this framework. How research interacts with the hypothesis space helps demarcate it. For example, if a hypothesis reduces the scope, it becomes more confirmatory, and trying to understand data given the current scope would be more exploratory work. This further could help demarcate when exploration is useful. Future theoretical work can detail how different types of research impact the hypothesis space in more detail.

11.6. Understanding when multiverse analyses are needed

Researchers writing a preregistration may face many degrees of freedom they have to choose from, and different researchers may motivate different choices. If, when writing such a preregistration, there appears to be little evidential support for certain degrees of freedom over others, the researcher is left with the option to either make more auxiliary assumptions or identify when an investigation into the pipeline scope is necessary by conducting a multiverse analysis that tests the impact of the different degrees of freedom on the result (see [ 8 ]). Thus, when applying this framework to explicitly state what pipeline variables are part of the hypothesis or an auxiliary assumption, the researcher can identify when it might be appropriate to conduct a multiverse analysis because they are having difficulty formulating hypotheses.

11.7. Describing novelty

Academic journals and research funders often ask for novelty, but the term ‘novelty’ can be vague and open to various interpretations [ 55 ]. This framework can be used to help justify the novelty of research. For example, consider a scenario where a previous study has established a psychological construct (e.g. well-being) that correlates with a certain outcome measure (e.g. long-term positive health outcomes). This framework can be used to explicitly justify novelty by (i) providing a more precise understanding of the relationship (e.g. linear or linear–plateau) or (ii) identifying more specific variables related to well-being or health outcomes. Stating how some research is novel is clearer than merely stating that the work is novel. This practice might even help journals and funders identify what type of novelty they would like to reward. In sum, this framework can help identify and articulate how research is novel.

11.8. Help to identify when standardization of pipelines is beneficial or problematic to a field

Many consider standardization in a field to be important for ensuring the comparability of results. Standardization of methods and tools entails that the pipeline P is identical (or at least very similar) across studies. However, in such cases, the standardized pipeline becomes an auxiliary assumption representing all possible pipelines. Therefore, while standardized pipelines have their benefits, this assumption becomes broader without validating (e.g. via multiverse analysis) which pipelines a standardized P represents. In summary, because this framework helps distinguish between auxiliary assumptions and explicit parts of the hypothesis and identifies when a multiverse analysis is needed, it can help determine when standardizations of pipelines are representative (narrower hypotheses) or assumptive (broader hypotheses).

12.  Conclusion

Here, we have argued that the scope of a hypothesis is made up of three dimensions: the relationship ( R ), variable ( XY ) and pipeline ( P ) selection. Along each of these dimensions, the scope can vary. Different types of scientific enterprises will often have hypotheses that vary the size of the scopes. We have argued that this focus on the scope of the hypothesis along these dimensions helps the hypothesis-maker formulate their hypotheses for preregistrations while also helping demarcate auxiliary hypotheses (assumed to be true) from the hypothesis (those being evaluated during the scientific process).

Hypotheses are an essential part of the scientific process. Considering what type of hypothesis is sufficient or relevant is an essential job of the researcher that we think has been overlooked. We hope this work promotes an understanding of what a hypothesis is and how its formulation and reduction in scope is an integral part of scientific progress. We hope it also helps clarify how broad hypotheses need not be vague or inappropriate.

Finally, we applied this idea of scopes to scientific progress and considered how to formulate an appropriate hypothesis. We have also listed several ways researchers can practically implement this framework today. However, there are other practicalities of this framework that future work should explore. For example, it could be used to differentiate and demarcate different scientific contributions (e.g. confirmatory studies, exploration studies, validation studies) with how their hypotheses interact with the different dimensions of the hypothesis space. Further, linking hypotheses over time within this framework can be a foundation for open hypothesis-making by promoting explicit links to previous work and detailing the reduction of the hypothesis space. This framework helps quantify the contribution to the hypothesis space of different studies and helps clarify what aspects of hypotheses can be relevant at different times.

Acknowledgements

We thank Filip Gedin, Kristoffer Sundberg, Jens Fust, and James Steele for valuable feedback on earlier versions of this article. We also thank Mark Rubin and an unnamed reviewer for valuable comments that have improved the article.

1 While this is our intention, we cannot claim that every theory has been accommodated.

2 Similar requirements of science being able to evaluate the hypothesis can be found in pragmatism [ 22 ], logical positivism [ 23 ] and falsification [ 24 ].

3 Although when making inferences about a failed evaluation of a scientific hypothesis it is possible, due to underdetermination, to reject the auxiliary hypothesis instead of rejecting the hypothesis. However, that rejection occurs at a later inference stage. The evaluation using the scientific method aims to test the scientific hypothesis, not the auxiliary assumptions.

4 Although some have argued that this practice is not as problematic or questionable (see [ 34 , 35 ]).

5 Alternatively, theories sometimes expand their boundary conditions. A theory that was previously about ‘humans' can be used with a more inclusive boundary condition. Thus it is possible for the hypothesis-maker to use a theory about humans (decision making) and expand it to fruit flies or plants (see [ 43 ]).

6 A similarity exists here with Popper, where he uses set theory in a similar way to compare theories (not hypotheses). Popper also discusses how theories with overlapping sets but neither is a subset are also comparable (see [ 24 , §§32–34]). We do not exclude this possibility but can require additional assumptions.

7 When this could be unclear, we place the element within quotation marks.

8 Here, we have assumed that there is no interaction between these variables in variable selection. If an interaction between x 1 and x 2 is hypothesized, this should be viewed as a different variable compared to ‘ x 1 or x 2 ’. The motivation behind this is because the hypothesis ‘ x 1 or x 2 ’ is not a superset of the interaction (i.e. ‘ x 1 or x 2 ’ is not necessarily true when the interaction is true). The interaction should, in this case, be considered a third variable (e.g. I( x 1 , x 2 )) and the hypothesis ‘ x 1 or x 2 or I( x 1 , x 2 )’ is broader than ‘ x 1 or x 2 ’.

9 Or possibly ambiguous or inconclusive.

10 This formulation of scope is compatible with different frameworks from the philosophy of science. For example, by narrowing the scope would in a Popperian terminology mean prohibiting more basic statements (thus a narrower hypothesis has a higher degree of falsifiability). The reduction of scope in the relational dimension would in Popperian terminology mean increase in precision (e.g. a circle is more precise than an ellipse since circles are a subset of possible ellipses), whereas reduction in variable selection and pipeline dimension would mean increase universality (e.g. ‘all heavenly bodies' is more universal than just ‘planets') [ 24 ]. For Meehl the reduction of the relationship dimension would amount to decreasing the relative tolerance of a theory to the Spielraum [ 46 ] .

11 If there is no relationship between x and y , we do not need to test if there is a positive relationship. If we know there is a positive relationship between x and y , we do not need to test if there is a relationship. If we know there is a relationship but there is not a positive relationship, then it is possible that they have a negative relationship.

Data accessibility

Declaration of ai use.

We have not used AI-assisted technologies in creating this article.

Authors' contributions

W.H.T.: conceptualization, investigation, writing—original draft, writing—review and editing; S.S.: investigation, writing—original draft, writing—review and editing.

Both authors gave final approval for publication and agreed to be held accountable for the work performed therein.

Conflict of interest declaration

We declare we have no competing interests.

We received no funding for this study.

What Is a Hypothesis? (Science)

If...,Then...

Angela Lumsden/Getty Images

• Scientific Method
• Chemical Laws
• Periodic Table
• Projects & Experiments
• Biochemistry
• Physical Chemistry
• Medical Chemistry
• Chemistry In Everyday Life
• Famous Chemists
• Activities for Kids
• Abbreviations & Acronyms
• Weather & Climate
• Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
• B.A., Physics and Mathematics, Hastings College

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

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

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

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

Writing a Hypothesis

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

Null Hypothesis and Alternative Hypothesis

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

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

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

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

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

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

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

Example of a Hypothesis

Examples of a hypothesis include:

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

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.

Editage All Access is a subscription-based platform that unifies the best AI tools and services designed to speed up, simplify, and streamline every step of a researcher’s journey. The Editage All Access Pack is a one-of-a-kind subscription that unlocks full access to an AI writing assistant, literature recommender, journal finder, scientific illustration tool, and exclusive discounts on professional publication services from Editage.  

Based on 22+ years of experience in academia, Editage All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $14 a month !    

Related Posts

What are the Best Research Funding Sources

Inductive vs. Deductive Research Approach

• Resources Home 🏠
• Try SciSpace Copilot
• Search research papers
• Add Copilot Extension
• Try AI Detector
• Try Paraphraser
• Try Citation Generator
• April Papers
• June Papers
• July Papers

The Craft of Writing a Strong Hypothesis

Table of Contents

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

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

What is a Hypothesis?

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

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

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

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

Different Types of Hypotheses‌

Types of hypotheses

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

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

1. Null hypothesis

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

2. Alternative hypothesis

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

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

3. Simple hypothesis

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

4. Complex hypothesis

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

5. Associative and casual hypothesis

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

6. Empirical hypothesis

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

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

7. Statistical hypothesis

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

Characteristics of a Good Hypothesis

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

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

Separating a Hypothesis from a Prediction

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

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

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

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

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

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

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

2. Carry out a recce

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

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

3. Create a 3-dimensional hypothesis

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

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

4. Write the first draft

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

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

5. Proof your hypothesis

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

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

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

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

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

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

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

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

2. What is an example of hypothesis?

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

3. What is an example of null hypothesis?

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

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

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

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

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

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

7. Difference between research question and research hypothesis?

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

8. What is plural for hypothesis?

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

9. What is the red queen hypothesis?

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

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

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

11. When to reject null hypothesis?

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

You might also like

Consensus GPT vs. SciSpace GPT: Choose the Best GPT for Research

Literature Review and Theoretical Framework: Understanding the Differences

Types of Essays in Academic Writing - Quick Guide (2024)

Drawing a Hypothesis

DRAWING A HYPOTHESIS - A publication project by Nikolaus Gansterer

Drawing a Hypothesis is an exciting reader on the ontology of forms of visualisation and on the development of the diagrammatic perspective and its use in contemporary art, science and theory. In an intense process of exchange with artists and scientists, Nikolaus Gansterer reveals drawing figures as a media of research which enables the emergence of new narratives and ideas by tracing the speculative potential of diagrams. Based on a discursive analysis of found figures with the artist’s own diagrammatic maps and models, the invited authors create unique correlations between thinking and drawing. The book is a rich compendium of figures of thought, which moves from scientific representation through artistic interpretation and vice versa. Central hypotheses of the publication were later re-transformed into installations and/or performance lectures and presented in various occasions and formats.

Author's note:

"The idea for this book originated during a two-year research project at the Jan van Eyck Academie in the Netherlands. My longheld fascination for diagrams, maps, networks and the graphical forms of visualising complex associations prompted me to approach the field from an artistic point of view. How (else) could these visual artefacts be comprehended? This book has arisen from a five-year exchange with theoreticians, scientists and artists on the question of the hypothetical potential of diagrams. Here I sent my drawings to various interpreters with a request for a written interpretation (micrology), so that in turn I could react to their texts with diagrammatic drawings and models."

"The process worked until the potential for action was exhausted. Through this intensive exchange of thoughts, the most varying ideas, hypotheses, theses and interrelations developed, eventually achieving the form of captions, (sci-fi)stories, and longer essays on the themes of figure, drawing, hypothesis and diagram. The resulting contributions are of very different kinds, reflecting their authors’ particular fields of knowledge in the fractious borderland between art, science and fiction. The design of the book was developed by Simona Koch reflecting the language of classical scientific formats of publications and enquiring how a specific appearance influences the perception of the content itself."

Table of Contents: Index of Figures. - Drawing a Hypothesis (Preface), Nikolaus Gansterer. - A Line with Variable Direction, which Traces No Contour, and Delimits No Form, Susanne Leeb. - I Must Be Seeing Things, Clemens Krümmel. - Subjective Objectivities, Jörg Piringer. - Grapheus Was Here, Anthony Auerbach. - Asynchronous Connections, Kirsten Matheus. - Distancing the If and Then, Emma Cocker. - Drawing Interest / Recording Vitality, Karin Harrasser.  - Nonself Compatibility in Plants, Monika Bakke. - Hypotheses non Fingo or When Symbols Fail, Andreas Schinner, - Wiry Fantasy, Ferdinand Schmatz. - Reading Figures, Helmut Leder. - Collection of Figures of Thoughts, Gerhard Dirmoser. - Radical Cartographies, Philippe Rekacewicz. - 3 Elements, Axel Stockburger. - Dances of Space, Marc Boeckler. - Collection of Emotions and Orientation, Christian Reder.  - On the Importance of Scientific Research in Relation to Humanities, Walter Seidl. - Interpersonal Governance Structures, Katja Mayer. - The Afterthought of Drawing: 6 Hypotheses, Jane Tormey. - The Hand, The Creatures, The Singing Garden & The Night Sky, Moira Roth. - The Unthought Known, Felix de Mendelssohn. - Processing the Routes of Thoughts, Kerstin Bartels. - An Attempted Survey, Section.a. - The Line of Thought, Hanneke Grootenboer. - Strong Evidence for Telon-priming Cell Layers in the Mammalian Olfactory Bulb, M. L. Nardo, A. Adam, P. Brandlmayr, B. F. Fisher. - Expected Anomalies Caused by Increased Radiation, Christina Stadlbauer. - On Pluto 86 Winter Lasts 92 Years, Ralo Mayer.  – Appendix: Personalia. Subindex. Index of Names. Colophon. Notices.

Book presentations with a performance-lecture and/or installation: – 22 Sept 2011: MHKA, Antwerp, Belgium. – 27 Oct 2011: KNAW, Amsterdam, The Netherlands. – 18 Nov 2011: Kunsthall, Bergen, Norway. – 23 Nov 2011: Kunsthalle Project Space, Vienna, Austria.

– March - April 2011: Galerie Lisi Haemmerle, Bregenz – 02 Feb 2012: "Die Materialität der Diagramme", NGBK, Berlin, Germany.

– 03 Feb - 14 March 2012: Archive Books in Berlin , Germany.

– 16 March 2012: "Leipzig book fair", Leipzig, Germany.

– 13 April - 12 May 2012: "A study on Knowledge", Forum Stadtpark, Graz, Austria.

– 20 Sept 2012: Lehrerzimmer, Bern, Switzerland.

– 9 Nov 2012 - 27 Jan 2013 : "Schaubilder", Kunstverein Bielefeld, Bielefeld, Germany.

– 23 Nov 2012: Salon fuer Kunstbuch at the Museum "21er Haus" , Vienna, Austria.

– 17 Nov 2012- 24 Feb, 2013: "World Book Design" exhibition, Printing Museum, Tokyo, Japan.

– 20 March 2013: Subnet Talk, at the KunstQuartier, Salzburg, Austria.

– Sept - Dec 2013: 4th Athens Biennale, Athens, Greece.


– May - Sept 2013: "When Thought becomes Matter...", Kunstraum Niederösterreich, Vienna, Austria.

– 2014: "Inventing Temperature", KCC, London, UK.

– 2014: "My Brain is in my Inkstand", Cranbrook Museum, Detroit, US.

The second corrected edition was released in Spring 2017

5 May 2017, 19:00 – 21:00

“PUBLISHING IN THE CONTEXT OF ARTISTIC RESEARCH - A BOOKISH TABLE TALK”

Launch of the brand new second edition of Drawing  a Hypothesis – Figures of Thought and its sequel publication Choreo-graphic Figures: Deviations from the Line .

Public presentation of a series of new publications discussing the critical role of documenting and publishing in the context of artistic research with invited guests and authors ao. Alex Arteaga, Emma Cocker, Alexander Damianisch, Nikolaus Gansterer, Mariella Greil, Lilia Mestre at AILab – Angewandte Innovation Lab , Franz-Josefs-Kai 3, 1010 Vienna, Austria. 

The publication could be ORDERED HERE!   Please download sample pages HERE!

"Drawing a Hypothesis – Figures of Thought", Springer Verlag Wien/NewYork, Edition Angewandte, 1st Edition, 2011.

ISBN 978-3-7091-0802-4 Library of Congress Control Number: 2011927923

"Drawing a Hypothesis – Figures of Thought", De Gruyter Berlin/Boston, Edition Angewandte,

2nd corrected edition, 2017.

ISBN 978-3-11-054661-3

352 p. 202 illus., 42 in colour. 1 folding map, Softcover

Book concept: Nikolaus Gansterer Book design: Simona Koch All drawings by Nikolaus Gansterer, 2005 - 2011.

Translation: Veronica Buckley, Aileen Derieg Proofreading: Dorrie Tattersall, Petra van der Jeught, Michael Karassowitsch Image proofreading and technical assistance: Jo Frenken

Photography: Nikolaus Gansterer, TimTom

Support: The publication was made possible by the generous funding of the Jan van Eyck Academie, Maaastricht, The Netherlands, the University of Applied Arts, Vienna, Austria and the BKA.

Keywords: Artistic research; Drawing; Diagrams; Figures of thought; Speculative Thinking.

The publication "Drawing a Hypothesis" was honoured to be one of the ten most beautiful books of Austria 2011 and won the bronze medal at the annual book design competition “ Best Books from all over the World - 2012”. The ceremony took place on the 16th of March 2012 at the Leipzig book fair in Germany.

Reviews (click to download files):

– Book Review in Interface by Mark Robert Doyle (in English), 2011

– Book Review by Gert Hasenhuetl (in English), 2012

– Book Review by Gert Hasenhuetl (in German), 2012

– Book Review by Marc Goethals (in Dutch), 2011

– Interview in Radio Orange with Natascha Gruber (in German), 2012

– Interview in Radio Oe1 with Hans Groisz (in German), 2012

– Performance Review: "Gedanken zeichnen / Drawing Thoughts", Roland Fischer, in: Der Bund Online, 21.09.2012

– "The Hand & The Creature" (Nikolaus Gansterer & Moira Roth), by guest editor Maria Fusco, in: "Discipline art journal / Issue 2, Autumn, 2012, Nick Croggon and Helen Hughes (Eds.), Melbourne.

– Nova Benway, Curatorial Assistant at the Drawing Center / New York in conversation with Nikolaus Gansterer, The Bottom Line Blog, 2 May 2013

– "Strukturbilder", Marie Beschorner, Vira Haglund, Cynthia Krell, Oliver M. Reuter, Lars Zumbansen, in: Kunst+Unterricht Nr. 376, Friedrich Verlag, Velber, 2013.

– Schaubilder, Eva Scharrer, in: Frieze, 2013

– Nina Samuel in conversation with Kaegan Sparks, The Drawing Center, New York, in: The Bottom Line, 01/2014

– Interview with Lucy Liu and Matthew Bohne, "Diagrams without context", PROPS Magazine#6, 2016

In advance of the third program in The Drawing Center’s Drafts series, Curatorial Assistant Nova Benway speaks with Vienna-based artist Nikolaus Gansterer about the generative potential of diagrams at the The Bottom Line Blog . Nova Benway: You just had an exhibition in Germany at the Kunstverein Bielefeld titled Schaubilder. Since your work deals with visibility and invisibility, let’s start with the question of what you showed.

Nikolaus Gansterer: I was showing work resulting from my project Drawing a Hypothesis – Figures of Thought (excerpt). For years I have had a strong fascination with diagrams (in German “Schaubilder”) and I was questioning how these relational visual artifacts—graphic forms visualizing complex associations—could be comprehended from an artistic point of view. In an intensive exchange with artists and scientists, I developed new forms of narratives and hypotheses by tracing the speculative potential of diagrams. Based on a discursive process, I sent my drawings to various interpreters with a request for a written interpretation (which I call “micrology”), so that in return I could react to their texts with diagrammatic drawings and models. In 2011 a publication resulted from this five-year exchange of figures of thought and figures of speech, describing, from various angles, the reflexive and dynamic character of diagrams.

My work shown at the exhibition at the Kunstverein Biefeld bears the title Table of Contents—literally displaying an arrangement of key figures of thought distilled/resulting from these inspiring conversations and transferred into fragile materiality. Here, drawing plays a crucial role in producing and communicating our knowledge(s), due to its ability to mediate between perception and reflection. For me drawing is a way to watch the mind working in the making of ideas, revealing thinking as an inter-subjective and translational process. It’s a balancing between visibility and invisibility.

NB: What constitutes a “figure of thought” for you? Does this form have definable parameters, or is it something more vague and intuitive?

NG: For me a “figure of thought” describes something dynamic and flexible, shifting rather than solid and static. My conception of the figure and figuration is deeply rooted in the Greek understanding of the term, which has a choreographic and performative notion, like “a body’s gesture caught in motion.” (See also Roland Barthes: A Lover’s Discourse, 1979.) It is both an elusive and highly lively form and, for me as artist, also a method to frame, name, and question a phenomenon by entering the field of my inquiry with a specific attitude, attention, and awareness. Due to the ambivalent character of the figure of thought, it’s interesting to use it as a vehicle and specific set of frames—maybe comparable to a system of lenses—to operate with.

NB: Can you give examples of interpretations your collaborators made for Drawing a Hypothesis that were particularly interesting to you?

NG: The multitude of explanations was most striking to me. In this project the potential of drawings and diagrams to activate the mind comes clearly to the forefront. I would argue that a diagram is a reflexive sign, empowering the reader in the process of reading and sense-making as it functions in a non-linear way. Thus it is probably closer to the nature of how our mind is organized and operates. Here again the performative character of diagrams plays an essential role. For example, the artist and theoretician Jane Tormey allowed herself to delve with all senses into the drawings and started living within and between the drawn lines. Letting herself be guided by the lines of thought, she directly inscribed her reflections onto the drawings and thus avoided simplistic description.[i]  On the contrary, the radical cartographers Philippe Rekacewicz and Marc Boeckler individually delineated a set of witty captions, reflecting on our basic need for spatio-temporal representations by deconstructing mapping as a practice of topological narrations. The writers Moira Roth and Ferdinand Schmatz wrote beautiful poetic micrologies on modes in-between seeing and sensing. Systems analyst Gerhard Dirmoser developed an extensive alphabet of figures of thought—which I re-translated into a fold-out map. All these written hypotheses served for me as another starting point to develop new drawings, models, installations, or a series of gestures.

NB: What kind of new knowledge do you think was produced?

NG: In my work, the intuitive part of knowing is as vital as the so-called cognitive part. Drawing—which is for me always a performative act in time and space—offered a way to combine these modes of thinking and sensing (in) correlations. Based on my method of “reverse engineering a theory” (by initiating the process of knowing through a speculative approach to reading diagrams, inferring the information they represent), the resulting hypotheses are naturally of very different kinds, reflecting their authors’ particular fields of knowledge in this fractious zone between art, science, and fiction. Each collated reflection—be it a theoretical essay, a poem, or a drawing—produces a very specific form of knowledge, revealing an enticing glance into our sub/consciousness and the possible mental spaces between recognizing and naming. For me “not-(yet)-knowing” is more exciting and inspiring than mere knowing.

NB: You have also done this kind of interpretation live, correct? How does that change the process?

NG: In the last few years I have collaborated with theoreticians and artists in a series of performances which I call TransLectures. Often a text, a drawing, or a material marks the starting point for different layers of interpretations and re-translations. Here drawing—for me a medium of high immediacy—could turn the subject into a score, an ad-hoc diagram, a makeshift model, or an instruction for taking action. Within these performances the process of “drawing beyond drawing” is central and extended along the categories of time, space, and movement: a line of thought becomes a line on the paper, a line in space, a line verbalized, or even a line drawn with the whole body. Together with the writer Emma Cocker I developed a series called Drawing on Drawing a Hypothesis. Using processes of cross-reading and live drawing, we dissected my publication Drawing a Hypothesis in the search for key words and phrases and evocative fragments in order to re-edit its content live. I am now preparing for the next step of TransLectures, which will take place in July at a performance festival in Berlin called Foreign Affairs. Invited philosophers, sociologists, and economists will discuss the omnipresent phenomenon of betting and the desire for speculation, but also the enormous impact of the idea of “futures” on social interrelations. Parallel to the lectures I will be live transforming the speakers’ ideas into ad hoc diagrams and daring card house models (“bodies of theory”) by translating their ideas into fragile forms of materiality.

NB: Your work sounds perfectly suited to Drafts, the program series at The Drawing Center you’ve recently taken part in. Can you describe your participation, and what was interesting for you about the process?

NG: It was indeed a very exciting process. I felt familiar with the rather associative approach of the “cadavre exquis” applied to Drafts through my research into diagrams. It was fascinating to find new visual material relating to explanations and visualizations in the archives of the Reanimation Library. In my first investigation I felt drawn to figures that are rather abstract and open. In my intense exchange with Kaegan Sparks, we worked on a set of figures which, in the end, contained both movements: an “informed openness” combined with a specific speculative and poetic potential. [i] “If I enter the drawing and start living in this world, I can describe this other reality as if I were looking at the ‘scene’ as it unfold before me,” Jane Tormey, “The Afterthought of Drawing: 6 Hypotheses” in Drawing a Hypothesis, pp. 241-258.

Theories, Hypotheses, and Laws: Definitions, examples, and their roles in science

by Anthony Carpi, Ph.D., Anne E. Egger, Ph.D.

Listen to this reading

Did you know that the idea of evolution had been part of Western thought for more than 2,000 years before Charles Darwin was born? Like many theories, the theory of evolution was the result of the work of many different scientists working in different disciplines over a period of time.

A scientific theory is an explanation inferred from multiple lines of evidence for some broad aspect of the natural world and is logical, testable, and predictive.

As new evidence comes to light, or new interpretations of existing data are proposed, theories may be revised and even change; however, they are not tenuous or speculative.

A scientific hypothesis is an inferred explanation of an observation or research finding; while more exploratory in nature than a theory, it is based on existing scientific knowledge.

A scientific law is an expression of a mathematical or descriptive relationship observed in nature.

Imagine yourself shopping in a grocery store with a good friend who happens to be a chemist. Struggling to choose between the many different types of tomatoes in front of you, you pick one up, turn to your friend, and ask her if she thinks the tomato is organic . Your friend simply chuckles and replies, "Of course it's organic!" without even looking at how the fruit was grown. Why the amused reaction? Your friend is highlighting a simple difference in vocabulary. To a chemist, the term organic refers to any compound in which hydrogen is bonded to carbon. Tomatoes (like all plants) are abundant in organic compounds – thus your friend's laughter. In modern agriculture, however, organic has come to mean food items grown or raised without the use of chemical fertilizers, pesticides, or other additives.

So who is correct? You both are. Both uses of the word are correct, though they mean different things in different contexts. There are, of course, lots of words that have more than one meaning (like bat , for example), but multiple meanings can be especially confusing when two meanings convey very different ideas and are specific to one field of study.

• Scientific theories

The term theory also has two meanings, and this double meaning often leads to confusion. In common language, the term theory generally refers to speculation or a hunch or guess. You might have a theory about why your favorite sports team isn't playing well, or who ate the last cookie from the cookie jar. But these theories do not fit the scientific use of the term. In science, a theory is a well-substantiated and comprehensive set of ideas that explains a phenomenon in nature. A scientific theory is based on large amounts of data and observations that have been collected over time. Scientific theories can be tested and refined by additional research , and they allow scientists to make predictions. Though you may be correct in your hunch, your cookie jar conjecture doesn't fit this more rigorous definition.

All scientific disciplines have well-established, fundamental theories . For example, atomic theory describes the nature of matter and is supported by multiple lines of evidence from the way substances behave and react in the world around us (see our series on Atomic Theory ). Plate tectonic theory describes the large scale movement of the outer layer of the Earth and is supported by evidence from studies about earthquakes , magnetic properties of the rocks that make up the seafloor , and the distribution of volcanoes on Earth (see our series on Plate Tectonic Theory ). The theory of evolution by natural selection , which describes the mechanism by which inherited traits that affect survivability or reproductive success can cause changes in living organisms over generations , is supported by extensive studies of DNA , fossils , and other types of scientific evidence (see our Charles Darwin series for more information). Each of these major theories guides and informs modern research in those fields, integrating a broad, comprehensive set of ideas.

So how are these fundamental theories developed, and why are they considered so well supported? Let's take a closer look at some of the data and research supporting the theory of natural selection to better see how a theory develops.

Comprehension Checkpoint

• The development of a scientific theory: Evolution and natural selection

The theory of evolution by natural selection is sometimes maligned as Charles Darwin 's speculation on the origin of modern life forms. However, evolutionary theory is not speculation. While Darwin is rightly credited with first articulating the theory of natural selection, his ideas built on more than a century of scientific research that came before him, and are supported by over a century and a half of research since.

• The Fixity Notion: Linnaeus

Figure 1: Cover of the 1760 edition of Systema Naturae .

Research about the origins and diversity of life proliferated in the 18th and 19th centuries. Carolus Linnaeus , a Swedish botanist and the father of modern taxonomy (see our module Taxonomy I for more information), was a devout Christian who believed in the concept of Fixity of Species , an idea based on the biblical story of creation. The Fixity of Species concept said that each species is based on an ideal form that has not changed over time. In the early stages of his career, Linnaeus traveled extensively and collected data on the structural similarities and differences between different species of plants. Noting that some very different plants had similar structures, he began to piece together his landmark work, Systema Naturae, in 1735 (Figure 1). In Systema , Linnaeus classified organisms into related groups based on similarities in their physical features. He developed a hierarchical classification system , even drawing relationships between seemingly disparate species (for example, humans, orangutans, and chimpanzees) based on the physical similarities that he observed between these organisms. Linnaeus did not explicitly discuss change in organisms or propose a reason for his hierarchy, but by grouping organisms based on physical characteristics, he suggested that species are related, unintentionally challenging the Fixity notion that each species is created in a unique, ideal form.

• The age of Earth: Leclerc and Hutton

Also in the early 1700s, Georges-Louis Leclerc, a French naturalist, and James Hutton , a Scottish geologist, began to develop new ideas about the age of the Earth. At the time, many people thought of the Earth as 6,000 years old, based on a strict interpretation of the events detailed in the Christian Old Testament by the influential Scottish Archbishop Ussher. By observing other planets and comets in the solar system , Leclerc hypothesized that Earth began as a hot, fiery ball of molten rock, mostly consisting of iron. Using the cooling rate of iron, Leclerc calculated that Earth must therefore be at least 70,000 years old in order to have reached its present temperature.

Hutton approached the same topic from a different perspective, gathering observations of the relationships between different rock formations and the rates of modern geological processes near his home in Scotland. He recognized that the relatively slow processes of erosion and sedimentation could not create all of the exposed rock layers in only a few thousand years (see our module The Rock Cycle ). Based on his extensive collection of data (just one of his many publications ran to 2,138 pages), Hutton suggested that the Earth was far older than human history – hundreds of millions of years old.

While we now know that both Leclerc and Hutton significantly underestimated the age of the Earth (by about 4 billion years), their work shattered long-held beliefs and opened a window into research on how life can change over these very long timescales.

• Fossil studies lead to the development of a theory of evolution: Cuvier

Figure 2: Illustration of an Indian elephant jaw and a mammoth jaw from Cuvier's 1796 paper.

With the age of Earth now extended by Leclerc and Hutton, more researchers began to turn their attention to studying past life. Fossils are the main way to study past life forms, and several key studies on fossils helped in the development of a theory of evolution . In 1795, Georges Cuvier began to work at the National Museum in Paris as a naturalist and anatomist. Through his work, Cuvier became interested in fossils found near Paris, which some claimed were the remains of the elephants that Hannibal rode over the Alps when he invaded Rome in 218 BCE . In studying both the fossils and living species , Cuvier documented different patterns in the dental structure and number of teeth between the fossils and modern elephants (Figure 2) (Horner, 1843). Based on these data , Cuvier hypothesized that the fossil remains were not left by Hannibal, but were from a distinct species of animal that once roamed through Europe and had gone extinct thousands of years earlier: the mammoth. The concept of species extinction had been discussed by a few individuals before Cuvier, but it was in direct opposition to the Fixity of Species concept – if every organism were based on a perfectly adapted, ideal form, how could any cease to exist? That would suggest it was no longer ideal.

While his work provided critical evidence of extinction , a key component of evolution , Cuvier was highly critical of the idea that species could change over time. As a result of his extensive studies of animal anatomy, Cuvier had developed a holistic view of organisms , stating that the

number, direction, and shape of the bones that compose each part of an animal's body are always in a necessary relation to all the other parts, in such a way that ... one can infer the whole from any one of them ...

In other words, Cuvier viewed each part of an organism as a unique, essential component of the whole organism. If one part were to change, he believed, the organism could not survive. His skepticism about the ability of organisms to change led him to criticize the whole idea of evolution , and his prominence in France as a scientist played a large role in discouraging the acceptance of the idea in the scientific community.

• Studies of invertebrates support a theory of change in species: Lamarck

Jean Baptiste Lamarck, a contemporary of Cuvier's at the National Museum in Paris, studied invertebrates like insects and worms. As Lamarck worked through the museum's large collection of invertebrates, he was impressed by the number and variety of organisms . He became convinced that organisms could, in fact, change through time, stating that

... time and favorable conditions are the two principal means which nature has employed in giving existence to all her productions. We know that for her time has no limit, and that consequently she always has it at her disposal.

This was a radical departure from both the fixity concept and Cuvier's ideas, and it built on the long timescale that geologists had recently established. Lamarck proposed that changes that occurred during an organism 's lifetime could be passed on to their offspring, suggesting, for example, that a body builder's muscles would be inherited by their children.

As it turned out, the mechanism by which Lamarck proposed that organisms change over time was wrong, and he is now often referred to disparagingly for his "inheritance of acquired characteristics" idea. Yet despite the fact that some of his ideas were discredited, Lamarck established a support for evolutionary theory that others would build on and improve.

• Rock layers as evidence for evolution: Smith

In the early 1800s, a British geologist and canal surveyor named William Smith added another component to the accumulating evidence for evolution . Smith observed that rock layers exposed in different parts of England bore similarities to one another: These layers (or strata) were arranged in a predictable order, and each layer contained distinct groups of fossils . From this series of observations , he developed a hypothesis that specific groups of animals followed one another in a definite sequence through Earth's history, and this sequence could be seen in the rock layers. Smith's hypothesis was based on his knowledge of geological principles , including the Law of Superposition.

The Law of Superposition states that sediments are deposited in a time sequence, with the oldest sediments deposited first, or at the bottom, and newer layers deposited on top. The concept was first expressed by the Persian scientist Avicenna in the 11th century, but was popularized by the Danish scientist Nicolas Steno in the 17th century. Note that the law does not state how sediments are deposited; it simply describes the relationship between the ages of deposited sediments.

Figure 3: Engraving from William Smith's 1815 monograph on identifying strata by fossils.

Smith backed up his hypothesis with extensive drawings of fossils uncovered during his research (Figure 3), thus allowing other scientists to confirm or dispute his findings. His hypothesis has, in fact, been confirmed by many other scientists and has come to be referred to as the Law of Faunal Succession. His work was critical to the formation of evolutionary theory as it not only confirmed Cuvier's work that organisms have gone extinct , but it also showed that the appearance of life does not date to the birth of the planet. Instead, the fossil record preserves a timeline of the appearance and disappearance of different organisms in the past, and in doing so offers evidence for change in organisms over time.

• The theory of evolution by natural selection: Darwin and Wallace

It was into this world that Charles Darwin entered: Linnaeus had developed a taxonomy of organisms based on their physical relationships, Leclerc and Hutton demonstrated that there was sufficient time in Earth's history for organisms to change, Cuvier showed that species of organisms have gone extinct , Lamarck proposed that organisms change over time, and Smith established a timeline of the appearance and disappearance of different organisms in the geological record .

Figure 4: Title page of the 1859 Murray edition of the Origin of Species by Charles Darwin.

Charles Darwin collected data during his work as a naturalist on the HMS Beagle starting in 1831. He took extensive notes on the geology of the places he visited; he made a major find of fossils of extinct animals in Patagonia and identified an extinct giant ground sloth named Megatherium . He experienced an earthquake in Chile that stranded beds of living mussels above water, where they would be preserved for years to come.

Perhaps most famously, he conducted extensive studies of animals on the Galápagos Islands, noting subtle differences in species of mockingbird, tortoise, and finch that were isolated on different islands with different environmental conditions. These subtle differences made the animals highly adapted to their environments .

This broad spectrum of data led Darwin to propose an idea about how organisms change "by means of natural selection" (Figure 4). But this idea was not based only on his work, it was also based on the accumulation of evidence and ideas of many others before him. Because his proposal encompassed and explained many different lines of evidence and previous work, they formed the basis of a new and robust scientific theory regarding change in organisms – the theory of evolution by natural selection .

Darwin's ideas were grounded in evidence and data so compelling that if he had not conceived them, someone else would have. In fact, someone else did. Between 1858 and 1859, Alfred Russel Wallace , a British naturalist, wrote a series of letters to Darwin that independently proposed natural selection as the means for evolutionary change. The letters were presented to the Linnean Society of London, a prominent scientific society at the time (see our module on Scientific Institutions and Societies ). This long chain of research highlights that theories are not just the work of one individual. At the same time, however, it often takes the insight and creativity of individuals to put together all of the pieces and propose a new theory . Both Darwin and Wallace were experienced naturalists who were familiar with the work of others. While all of the work leading up to 1830 contributed to the theory of evolution , Darwin's and Wallace's theory changed the way that future research was focused by presenting a comprehensive, well-substantiated set of ideas, thus becoming a fundamental theory of biological research.

• Expanding, testing, and refining scientific theories
• Genetics and evolution: Mendel and Dobzhansky

Since Darwin and Wallace first published their ideas, extensive research has tested and expanded the theory of evolution by natural selection . Darwin had no concept of genes or DNA or the mechanism by which characteristics were inherited within a species . A contemporary of Darwin's, the Austrian monk Gregor Mendel , first presented his own landmark study, Experiments in Plant Hybridization, in 1865 in which he provided the basic patterns of genetic inheritance , describing which characteristics (and evolutionary changes) can be passed on in organisms (see our Genetics I module for more information). Still, it wasn't until much later that a "gene" was defined as the heritable unit.

In 1937, the Ukrainian born geneticist Theodosius Dobzhansky published Genetics and the Origin of Species , a seminal work in which he described genes themselves and demonstrated that it is through mutations in genes that change occurs. The work defined evolution as "a change in the frequency of an allele within a gene pool" ( Dobzhansky, 1982 ). These studies and others in the field of genetics have added to Darwin's work, expanding the scope of the theory .

• Evolution under a microscope: Lenski

More recently, Dr. Richard Lenski, a scientist at Michigan State University, isolated a single Escherichia coli bacterium in 1989 as the first step of the longest running experimental test of evolutionary theory to date – a true test meant to replicate evolution and natural selection in the lab.

After the single microbe had multiplied, Lenski isolated the offspring into 12 different strains , each in their own glucose-supplied culture, predicting that the genetic make-up of each strain would change over time to become more adapted to their specific culture as predicted by evolutionary theory . These 12 lines have been nurtured for over 40,000 bacterial generations (luckily bacterial generations are much shorter than human generations) and exposed to different selective pressures such as heat , cold, antibiotics, and infection with other microorganisms. Lenski and colleagues have studied dozens of aspects of evolutionary theory with these genetically isolated populations . In 1999, they published a paper that demonstrated that random genetic mutations were common within the populations and highly diverse across different individual bacteria . However, "pivotal" mutations that are associated with beneficial changes in the group are shared by all descendants in a population and are much rarer than random mutations, as predicted by the theory of evolution by natural selection (Papadopoulos et al., 1999).

• Punctuated equilibrium: Gould and Eldredge

While established scientific theories like evolution have a wealth of research and evidence supporting them, this does not mean that they cannot be refined as new information or new perspectives on existing data become available. For example, in 1972, biologist Stephen Jay Gould and paleontologist Niles Eldredge took a fresh look at the existing data regarding the timing by which evolutionary change takes place. Gould and Eldredge did not set out to challenge the theory of evolution; rather they used it as a guiding principle and asked more specific questions to add detail and nuance to the theory. This is true of all theories in science: they provide a framework for additional research. At the time, many biologists viewed evolution as occurring gradually, causing small incremental changes in organisms at a relatively steady rate. The idea is referred to as phyletic gradualism , and is rooted in the geological concept of uniformitarianism . After reexamining the available data, Gould and Eldredge came to a different explanation, suggesting that evolution consists of long periods of stability that are punctuated by occasional instances of dramatic change – a process they called punctuated equilibrium .

Like Darwin before them, their proposal is rooted in evidence and research on evolutionary change, and has been supported by multiple lines of evidence. In fact, punctuated equilibrium is now considered its own theory in evolutionary biology. Punctuated equilibrium is not as broad of a theory as natural selection . In science, some theories are broad and overarching of many concepts, such as the theory of evolution by natural selection; others focus on concepts at a smaller, or more targeted, scale such as punctuated equilibrium. And punctuated equilibrium does not challenge or weaken the concept of natural selection; rather, it represents a change in our understanding of the timing by which change occurs in organisms , and a theory within a theory. The theory of evolution by natural selection now includes both gradualism and punctuated equilibrium to describe the rate at which change proceeds.

• Hypotheses and laws: Other scientific concepts

One of the challenges in understanding scientific terms like theory is that there is not a precise definition even within the scientific community. Some scientists debate over whether certain proposals merit designation as a hypothesis or theory , and others mistakenly use the terms interchangeably. But there are differences in these terms. A hypothesis is a proposed explanation for an observable phenomenon. Hypotheses , just like theories , are based on observations from research . For example, LeClerc did not hypothesize that Earth had cooled from a molten ball of iron as a random guess; rather, he developed this hypothesis based on his observations of information from meteorites.

A scientist often proposes a hypothesis before research confirms it as a way of predicting the outcome of study to help better define the parameters of the research. LeClerc's hypothesis allowed him to use known parameters (the cooling rate of iron) to do additional work. A key component of a formal scientific hypothesis is that it is testable and falsifiable. For example, when Richard Lenski first isolated his 12 strains of bacteria , he likely hypothesized that random mutations would cause differences to appear within a period of time in the different strains of bacteria. But when a hypothesis is generated in science, a scientist will also make an alternative hypothesis , an explanation that explains a study if the data do not support the original hypothesis. If the different strains of bacteria in Lenski's work did not diverge over the indicated period of time, perhaps the rate of mutation was slower than first thought.

So you might ask, if theories are so well supported, do they eventually become laws? The answer is no – not because they aren't well-supported, but because theories and laws are two very different things. Laws describe phenomena, often mathematically. Theories, however, explain phenomena. For example, in 1687 Isaac Newton proposed a Theory of Gravitation, describing gravity as a force of attraction between two objects. As part of this theory, Newton developed a Law of Universal Gravitation that explains how this force operates. This law states that the force of gravity between two objects is inversely proportional to the square of the distance between those objects. Newton 's Law does not explain why this is true, but it describes how gravity functions (see our Gravity: Newtonian Relationships module for more detail). In 1916, Albert Einstein developed his theory of general relativity to explain the mechanism by which gravity has its effect. Einstein's work challenges Newton's theory, and has been found after extensive testing and research to more accurately describe the phenomenon of gravity. While Einstein's work has replaced Newton's as the dominant explanation of gravity in modern science, Newton's Law of Universal Gravitation is still used as it reasonably (and more simply) describes the force of gravity under many conditions. Similarly, the Law of Faunal Succession developed by William Smith does not explain why organisms follow each other in distinct, predictable ways in the rock layers, but it accurately describes the phenomenon.

Choose Your Test

• Search Blogs By Category
• College Admissions
• AP and IB Exams
• GPA and Coursework

What Is a Hypothesis and How Do I Write One?

General Education

Think about something strange and unexplainable in your life. Maybe you get a headache right before it rains, or maybe you think your favorite sports team wins when you wear a certain color. If you wanted to see whether these are just coincidences or scientific fact, you would form a hypothesis, then create an experiment to see whether that hypothesis is true or not.

But what is a hypothesis, anyway? If you’re not sure about what a hypothesis is--or how to test for one!--you’re in the right place. This article will teach you everything you need to know about hypotheses, including: 

• Defining the term “hypothesis” 
• Providing hypothesis examples 
• Giving you tips for how to write your own hypothesis

So let’s get started!

What Is a Hypothesis?

Merriam Webster defines a hypothesis as “an assumption or concession made for the sake of argument.” In other words, a hypothesis is an educated guess . Scientists make a reasonable assumption--or a hypothesis--then design an experiment to test whether it’s true or not. Keep in mind that in science, a hypothesis should be testable. You have to be able to design an experiment that tests your hypothesis in order for it to be valid. 

As you could assume from that statement, it’s easy to make a bad hypothesis. But when you’re holding an experiment, it’s even more important that your guesses be good...after all, you’re spending time (and maybe money!) to figure out more about your observation. That’s why we refer to a hypothesis as an educated guess--good hypotheses are based on existing data and research to make them as sound as possible.

Hypotheses are one part of what’s called the scientific method .  Every (good) experiment or study is based in the scientific method. The scientific method gives order and structure to experiments and ensures that interference from scientists or outside influences does not skew the results. It’s important that you understand the concepts of the scientific method before holding your own experiment. Though it may vary among scientists, the scientific method is generally made up of six steps (in order):

• Observation
• Asking questions
• Forming a hypothesis
• Analyze the data
• Communicate your results

You’ll notice that the hypothesis comes pretty early on when conducting an experiment. That’s because experiments work best when they’re trying to answer one specific question. And you can’t conduct an experiment until you know what you’re trying to prove!

Independent and Dependent Variables 

After doing your research, you’re ready for another important step in forming your hypothesis: identifying variables. Variables are basically any factor that could influence the outcome of your experiment . Variables have to be measurable and related to the topic being studied.

There are two types of variables:  independent variables and dependent variables. I ndependent variables remain constant . For example, age is an independent variable; it will stay the same, and researchers can look at different ages to see if it has an effect on the dependent variable. 

Speaking of dependent variables... dependent variables are subject to the influence of the independent variable , meaning that they are not constant. Let’s say you want to test whether a person’s age affects how much sleep they need. In that case, the independent variable is age (like we mentioned above), and the dependent variable is how much sleep a person gets. 

Variables will be crucial in writing your hypothesis. You need to be able to identify which variable is which, as both the independent and dependent variables will be written into your hypothesis. For instance, in a study about exercise, the independent variable might be the speed at which the respondents walk for thirty minutes, and the dependent variable would be their heart rate. In your study and in your hypothesis, you’re trying to understand the relationship between the two variables.

Elements of a Good Hypothesis

The best hypotheses start by asking the right questions . For instance, if you’ve observed that the grass is greener when it rains twice a week, you could ask what kind of grass it is, what elevation it’s at, and if the grass across the street responds to rain in the same way. Any of these questions could become the backbone of experiments to test why the grass gets greener when it rains fairly frequently.

As you’re asking more questions about your first observation, make sure you’re also making more observations . If it doesn’t rain for two weeks and the grass still looks green, that’s an important observation that could influence your hypothesis. You'll continue observing all throughout your experiment, but until the hypothesis is finalized, every observation should be noted.

Finally, you should consult secondary research before writing your hypothesis . Secondary research is comprised of results found and published by other people. You can usually find this information online or at your library. Additionally, m ake sure the research you find is credible and related to your topic. If you’re studying the correlation between rain and grass growth, it would help you to research rain patterns over the past twenty years for your county, published by a local agricultural association. You should also research the types of grass common in your area, the type of grass in your lawn, and whether anyone else has conducted experiments about your hypothesis. Also be sure you’re checking the quality of your research . Research done by a middle school student about what minerals can be found in rainwater would be less useful than an article published by a local university.

Writing Your Hypothesis

Once you’ve considered all of the factors above, you’re ready to start writing your hypothesis. Hypotheses usually take a certain form when they’re written out in a research report.

When you boil down your hypothesis statement, you are writing down your best guess and not the question at hand . This means that your statement should be written as if it is fact already, even though you are simply testing it.

The reason for this is that, after you have completed your study, you'll either accept or reject your if-then or your null hypothesis. All hypothesis testing examples should be measurable and able to be confirmed or denied. You cannot confirm a question, only a statement! 

In fact, you come up with hypothesis examples all the time! For instance, when you guess on the outcome of a basketball game, you don’t say, “Will the Miami Heat beat the Boston Celtics?” but instead, “I think the Miami Heat will beat the Boston Celtics.” You state it as if it is already true, even if it turns out you’re wrong. You do the same thing when writing your hypothesis.

Additionally, keep in mind that hypotheses can range from very specific to very broad.  These hypotheses can be specific, but if your hypothesis testing examples involve a broad range of causes and effects, your hypothesis can also be broad.  

The Two Types of Hypotheses

Now that you understand what goes into a hypothesis, it’s time to look more closely at the two most common types of hypothesis: the if-then hypothesis and the null hypothesis.

#1: If-Then Hypotheses

First of all, if-then hypotheses typically follow this formula:

If ____ happens, then ____ will happen.

The goal of this type of hypothesis is to test the causal relationship between the independent and dependent variable. It’s fairly simple, and each hypothesis can vary in how detailed it can be. We create if-then hypotheses all the time with our daily predictions. Here are some examples of hypotheses that use an if-then structure from daily life: 

• If I get enough sleep, I’ll be able to get more work done tomorrow.
• If the bus is on time, I can make it to my friend’s birthday party. 
• If I study every night this week, I’ll get a better grade on my exam. 

In each of these situations, you’re making a guess on how an independent variable (sleep, time, or studying) will affect a dependent variable (the amount of work you can do, making it to a party on time, or getting better grades). 

You may still be asking, “What is an example of a hypothesis used in scientific research?” Take one of the hypothesis examples from a real-world study on whether using technology before bed affects children’s sleep patterns. The hypothesis read s:

“We hypothesized that increased hours of tablet- and phone-based screen time at bedtime would be inversely correlated with sleep quality and child attention.”

It might not look like it, but this is an if-then statement. The researchers basically said, “If children have more screen usage at bedtime, then their quality of sleep and attention will be worse.” The sleep quality and attention are the dependent variables and the screen usage is the independent variable. (Usually, the independent variable comes after the “if” and the dependent variable comes after the “then,” as it is the independent variable that affects the dependent variable.) This is an excellent example of how flexible hypothesis statements can be, as long as the general idea of “if-then” and the independent and dependent variables are present.

#2: Null Hypotheses

Your if-then hypothesis is not the only one needed to complete a successful experiment, however. You also need a null hypothesis to test it against. In its most basic form, the null hypothesis is the opposite of your if-then hypothesis . When you write your null hypothesis, you are writing a hypothesis that suggests that your guess is not true, and that the independent and dependent variables have no relationship .

One null hypothesis for the cell phone and sleep study from the last section might say: 

“If children have more screen usage at bedtime, their quality of sleep and attention will not be worse.” 

In this case, this is a null hypothesis because it’s asking the opposite of the original thesis! 

Conversely, if your if-then hypothesis suggests that your two variables have no relationship, then your null hypothesis would suggest that there is one. So, pretend that there is a study that is asking the question, “Does the amount of followers on Instagram influence how long people spend on the app?” The independent variable is the amount of followers, and the dependent variable is the time spent. But if you, as the researcher, don’t think there is a relationship between the number of followers and time spent, you might write an if-then hypothesis that reads:

“If people have many followers on Instagram, they will not spend more time on the app than people who have less.”

In this case, the if-then suggests there isn’t a relationship between the variables. In that case, one of the null hypothesis examples might say:

“If people have many followers on Instagram, they will spend more time on the app than people who have less.”

You then test both the if-then and the null hypothesis to gauge if there is a relationship between the variables, and if so, how much of a relationship. 

4 Tips to Write the Best Hypothesis

If you’re going to take the time to hold an experiment, whether in school or by yourself, you’re also going to want to take the time to make sure your hypothesis is a good one. The best hypotheses have four major elements in common: plausibility, defined concepts, observability, and general explanation.

#1: Plausibility

At first glance, this quality of a hypothesis might seem obvious. When your hypothesis is plausible, that means it’s possible given what we know about science and general common sense. However, improbable hypotheses are more common than you might think. 

Imagine you’re studying weight gain and television watching habits. If you hypothesize that people who watch more than  twenty hours of television a week will gain two hundred pounds or more over the course of a year, this might be improbable (though it’s potentially possible). Consequently, c ommon sense can tell us the results of the study before the study even begins.

Improbable hypotheses generally go against  science, as well. Take this hypothesis example: 

“If a person smokes one cigarette a day, then they will have lungs just as healthy as the average person’s.” 

This hypothesis is obviously untrue, as studies have shown again and again that cigarettes negatively affect lung health. You must be careful that your hypotheses do not reflect your own personal opinion more than they do scientifically-supported findings. This plausibility points to the necessity of research before the hypothesis is written to make sure that your hypothesis has not already been disproven.

#2: Defined Concepts

The more advanced you are in your studies, the more likely that the terms you’re using in your hypothesis are specific to a limited set of knowledge. One of the hypothesis testing examples might include the readability of printed text in newspapers, where you might use words like “kerning” and “x-height.” Unless your readers have a background in graphic design, it’s likely that they won’t know what you mean by these terms. Thus, it’s important to either write what they mean in the hypothesis itself or in the report before the hypothesis.

Here’s what we mean. Which of the following sentences makes more sense to the common person?

If the kerning is greater than average, more words will be read per minute.

If the space between letters is greater than average, more words will be read per minute.

For people reading your report that are not experts in typography, simply adding a few more words will be helpful in clarifying exactly what the experiment is all about. It’s always a good idea to make your research and findings as accessible as possible. 

Good hypotheses ensure that you can observe the results. 

#3: Observability

In order to measure the truth or falsity of your hypothesis, you must be able to see your variables and the way they interact. For instance, if your hypothesis is that the flight patterns of satellites affect the strength of certain television signals, yet you don’t have a telescope to view the satellites or a television to monitor the signal strength, you cannot properly observe your hypothesis and thus cannot continue your study.

Some variables may seem easy to observe, but if you do not have a system of measurement in place, you cannot observe your hypothesis properly. Here’s an example: if you’re experimenting on the effect of healthy food on overall happiness, but you don’t have a way to monitor and measure what “overall happiness” means, your results will not reflect the truth. Monitoring how often someone smiles for a whole day is not reasonably observable, but having the participants state how happy they feel on a scale of one to ten is more observable. 

In writing your hypothesis, always keep in mind how you'll execute the experiment.

#4: Generalizability 

Perhaps you’d like to study what color your best friend wears the most often by observing and documenting the colors she wears each day of the week. This might be fun information for her and you to know, but beyond you two, there aren’t many people who could benefit from this experiment. When you start an experiment, you should note how generalizable your findings may be if they are confirmed. Generalizability is basically how common a particular phenomenon is to other people’s everyday life.

Let’s say you’re asking a question about the health benefits of eating an apple for one day only, you need to realize that the experiment may be too specific to be helpful. It does not help to explain a phenomenon that many people experience. If you find yourself with too specific of a hypothesis, go back to asking the big question: what is it that you want to know, and what do you think will happen between your two variables?

Hypothesis Testing Examples

We know it can be hard to write a good hypothesis unless you’ve seen some good hypothesis examples. We’ve included four hypothesis examples based on some made-up experiments. Use these as templates or launch pads for coming up with your own hypotheses.

Experiment #1: Students Studying Outside (Writing a Hypothesis)

You are a student at PrepScholar University. When you walk around campus, you notice that, when the temperature is above 60 degrees, more students study in the quad. You want to know when your fellow students are more likely to study outside. With this information, how do you make the best hypothesis possible?

You must remember to make additional observations and do secondary research before writing your hypothesis. In doing so, you notice that no one studies outside when it’s 75 degrees and raining, so this should be included in your experiment. Also, studies done on the topic beforehand suggested that students are more likely to study in temperatures less than 85 degrees. With this in mind, you feel confident that you can identify your variables and write your hypotheses:

If-then: “If the temperature in Fahrenheit is less than 60 degrees, significantly fewer students will study outside.”

Null: “If the temperature in Fahrenheit is less than 60 degrees, the same number of students will study outside as when it is more than 60 degrees.”

These hypotheses are plausible, as the temperatures are reasonably within the bounds of what is possible. The number of people in the quad is also easily observable. It is also not a phenomenon specific to only one person or at one time, but instead can explain a phenomenon for a broader group of people.

To complete this experiment, you pick the month of October to observe the quad. Every day (except on the days where it’s raining)from 3 to 4 PM, when most classes have released for the day, you observe how many people are on the quad. You measure how many people come  and how many leave. You also write down the temperature on the hour. 

After writing down all of your observations and putting them on a graph, you find that the most students study on the quad when it is 70 degrees outside, and that the number of students drops a lot once the temperature reaches 60 degrees or below. In this case, your research report would state that you accept or “failed to reject” your first hypothesis with your findings.

Experiment #2: The Cupcake Store (Forming a Simple Experiment)

Let’s say that you work at a bakery. You specialize in cupcakes, and you make only two colors of frosting: yellow and purple. You want to know what kind of customers are more likely to buy what kind of cupcake, so you set up an experiment. Your independent variable is the customer’s gender, and the dependent variable is the color of the frosting. What is an example of a hypothesis that might answer the question of this study?

Here’s what your hypotheses might look like: 

If-then: “If customers’ gender is female, then they will buy more yellow cupcakes than purple cupcakes.”

Null: “If customers’ gender is female, then they will be just as likely to buy purple cupcakes as yellow cupcakes.”

This is a pretty simple experiment! It passes the test of plausibility (there could easily be a difference), defined concepts (there’s nothing complicated about cupcakes!), observability (both color and gender can be easily observed), and general explanation ( this would potentially help you make better business decisions ).

Experiment #3: Backyard Bird Feeders (Integrating Multiple Variables and Rejecting the If-Then Hypothesis)

While watching your backyard bird feeder, you realized that different birds come on the days when you change the types of seeds. You decide that you want to see more cardinals in your backyard, so you decide to see what type of food they like the best and set up an experiment. 

However, one morning, you notice that, while some cardinals are present, blue jays are eating out of your backyard feeder filled with millet. You decide that, of all of the other birds, you would like to see the blue jays the least. This means you'll have more than one variable in your hypothesis. Your new hypotheses might look like this: 

If-then: “If sunflower seeds are placed in the bird feeders, then more cardinals will come than blue jays. If millet is placed in the bird feeders, then more blue jays will come than cardinals.”

Null: “If either sunflower seeds or millet are placed in the bird, equal numbers of cardinals and blue jays will come.”

Through simple observation, you actually find that cardinals come as often as blue jays when sunflower seeds or millet is in the bird feeder. In this case, you would reject your “if-then” hypothesis and “fail to reject” your null hypothesis . You cannot accept your first hypothesis, because it’s clearly not true. Instead you found that there was actually no relation between your different variables. Consequently, you would need to run more experiments with different variables to see if the new variables impact the results.

Experiment #4: In-Class Survey (Including an Alternative Hypothesis)

You’re about to give a speech in one of your classes about the importance of paying attention. You want to take this opportunity to test a hypothesis you’ve had for a while: 

If-then: If students sit in the first two rows of the classroom, then they will listen better than students who do not.

Null: If students sit in the first two rows of the classroom, then they will not listen better or worse than students who do not.

You give your speech and then ask your teacher if you can hand out a short survey to the class. On the survey, you’ve included questions about some of the topics you talked about. When you get back the results, you’re surprised to see that not only do the students in the first two rows not pay better attention, but they also scored worse than students in other parts of the classroom! Here, both your if-then and your null hypotheses are not representative of your findings. What do you do?

This is when you reject both your if-then and null hypotheses and instead create an alternative hypothesis . This type of hypothesis is used in the rare circumstance that neither of your hypotheses is able to capture your findings . Now you can use what you’ve learned to draft new hypotheses and test again! 

Key Takeaways: Hypothesis Writing

The more comfortable you become with writing hypotheses, the better they will become. The structure of hypotheses is flexible and may need to be changed depending on what topic you are studying. The most important thing to remember is the purpose of your hypothesis and the difference between the if-then and the null . From there, in forming your hypothesis, you should constantly be asking questions, making observations, doing secondary research, and considering your variables. After you have written your hypothesis, be sure to edit it so that it is plausible, clearly defined, observable, and helpful in explaining a general phenomenon.

Writing a hypothesis is something that everyone, from elementary school children competing in a science fair to professional scientists in a lab, needs to know how to do. Hypotheses are vital in experiments and in properly executing the scientific method . When done correctly, hypotheses will set up your studies for success and help you to understand the world a little better, one experiment at a time.

What’s Next?

If you’re studying for the science portion of the ACT, there’s definitely a lot you need to know. We’ve got the tools to help, though! Start by checking out our ultimate study guide for the ACT Science subject test. Once you read through that, be sure to download our recommended ACT Science practice tests , since they’re one of the most foolproof ways to improve your score. (And don’t forget to check out our expert guide book , too.)

If you love science and want to major in a scientific field, you should start preparing in high school . Here are the science classes you should take to set yourself up for success.

If you’re trying to think of science experiments you can do for class (or for a science fair!), here’s a list of 37 awesome science experiments you can do at home

Trending Now

How to Get Into Harvard and the Ivy League

How to Get a Perfect 4.0 GPA

How to Write an Amazing College Essay

What Exactly Are Colleges Looking For?

ACT vs. SAT: Which Test Should You Take?

When should you take the SAT or ACT?

Get Your Free

Find Your Target SAT Score

Free Complete Official SAT Practice Tests

How to Get a Perfect SAT Score, by an Expert Full Scorer

Score 800 on SAT Math

Score 800 on SAT Reading and Writing

How to Improve Your Low SAT Score

Score 600 on SAT Math

Score 600 on SAT Reading and Writing

Find Your Target ACT Score

Complete Official Free ACT Practice Tests

How to Get a Perfect ACT Score, by a 36 Full Scorer

Get a 36 on ACT English

Get a 36 on ACT Math

Get a 36 on ACT Reading

Get a 36 on ACT Science

How to Improve Your Low ACT Score

Get a 24 on ACT English

Get a 24 on ACT Math

Get a 24 on ACT Reading

Get a 24 on ACT Science

Stay Informed

Get the latest articles and test prep tips!

Ashley Sufflé Robinson has a Ph.D. in 19th Century English Literature. As a content writer for PrepScholar, Ashley is passionate about giving college-bound students the in-depth information they need to get into the school of their dreams.

Ask a Question Below

Have any questions about this article or other topics? Ask below and we'll reply!

How to Write a Hypothesis? Types and Examples 

All research studies involve the use of the scientific method, which is a mathematical and experimental technique used to conduct experiments by developing and testing a hypothesis or a prediction about an outcome. Simply put, a hypothesis is a suggested solution to a problem. It includes elements that are expressed in terms of relationships with each other to explain a condition or an assumption that hasn’t been verified using facts. 1 The typical steps in a scientific method include developing such a hypothesis, testing it through various methods, and then modifying it based on the outcomes of the experiments.  

A research hypothesis can be defined as a specific, testable prediction about the anticipated results of a study. 2 Hypotheses help guide the research process and supplement the aim of the study. After several rounds of testing, hypotheses can help develop scientific theories. 3 Hypotheses are often written as if-then statements. 

Here are two hypothesis examples: 

Dandelions growing in nitrogen-rich soils for two weeks develop larger leaves than those in nitrogen-poor soils because nitrogen stimulates vegetative growth. 4  

If a company offers flexible work hours, then their employees will be happier at work. 5  

Table of Contents

• What is a hypothesis? 
• Types of hypotheses 
• Characteristics of a hypothesis 
• Functions of a hypothesis 
• How to write a hypothesis 
• Hypothesis examples 
• Frequently asked questions 

What is a hypothesis?

A hypothesis expresses an expected relationship between variables in a study and is developed before conducting any research. Hypotheses are not opinions but rather are expected relationships based on facts and observations. They help support scientific research and expand existing knowledge. An incorrectly formulated hypothesis can affect the entire experiment leading to errors in the results so it’s important to know how to formulate a hypothesis and develop it carefully.

A few sources of a hypothesis include observations from prior studies, current research and experiences, competitors, scientific theories, and general conditions that can influence people. Figure 1 depicts the different steps in a research design and shows where exactly in the process a hypothesis is developed. 4  

There are seven different types of hypotheses—simple, complex, directional, nondirectional, associative and causal, null, and alternative. 

Types of hypotheses

The seven types of hypotheses are listed below: 5 , 6,7  

• Simple : Predicts the relationship between a single dependent variable and a single independent variable. 

Example: Exercising in the morning every day will increase your productivity.  

• Complex : Predicts the relationship between two or more variables. 

Example: Spending three hours or more on social media daily will negatively affect children’s mental health and productivity, more than that of adults.  

• Directional : Specifies the expected direction to be followed and uses terms like increase, decrease, positive, negative, more, or less. 

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

• Non-directional : Does not predict the exact direction, nature, or magnitude of the relationship between two variables but rather states the existence of a relationship. This hypothesis may be used when there is no underlying theory or if findings contradict prior research. 

Example: Cats and dogs differ in the amount of affection they express.  

• Associative and causal : An associative hypothesis suggests an interdependency between variables, that is, how a change in one variable changes the other.  

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

A causal hypothesis, on the other hand, expresses a cause-and-effect association between variables. 

Example: Long-term alcohol use causes liver damage.  

• Null : Claims that the original hypothesis is false by showing that there is no relationship between the variables. 

Example: Sleep duration does not have any effect on productivity.  

• Alternative : States the opposite of the null hypothesis, that is, a relationship exists between two variables. 

Example: Sleep duration affects productivity.  

Characteristics of a hypothesis

So, what makes a good hypothesis? Here are some important characteristics of a hypothesis. 8,9  

• Testable : You must be able to test the hypothesis using scientific methods to either accept or reject the prediction. 
• Falsifiable : It should be possible to collect data that reject rather than support the hypothesis. 
• Logical : Hypotheses shouldn’t be a random guess but rather should be based on previous theories, observations, prior research, and logical reasoning. 
• Positive : The hypothesis statement about the existence of an association should be positive, that is, it should not suggest that an association does not exist. Therefore, the language used and knowing how to phrase a hypothesis is very important. 
• Clear and accurate : The language used should be easily comprehensible and use correct terminology. 
• Relevant : The hypothesis should be relevant and specific to the research question. 
• Structure : Should include all the elements that make a good hypothesis: variables, relationship, and outcome. 

Functions of a hypothesis

The following list mentions some important functions of a hypothesis: 1  

• Maintains the direction and progress of the research. 
• Expresses the important assumptions underlying the proposition in a single statement. 
• Establishes a suitable context for researchers to begin their investigation and for readers who are referring to the final report. 
• Provides an explanation for the occurrence of a specific phenomenon. 
• Ensures selection of appropriate and accurate facts necessary and relevant to the research subject. 

To summarize, a hypothesis provides the conceptual elements that complete the known data, conceptual relationships that systematize unordered elements, and conceptual meanings and interpretations that explain the unknown phenomena. 1  

How to write a hypothesis

Listed below are the main steps explaining how to write a hypothesis. 2,4,5  

• Make an observation and identify variables : Observe the subject in question and try to recognize a pattern or a relationship between the variables involved. This step provides essential background information to begin your research.  

For example, if you notice that an office’s vending machine frequently runs out of a specific snack, you may predict that more people in the office choose that snack over another. 

• Identify the main research question : After identifying a subject and recognizing a pattern, the next step is to ask a question that your hypothesis will answer.  

For example, after observing employees’ break times at work, you could ask “why do more employees take breaks in the morning rather than in the afternoon?” 

• Conduct some preliminary research to ensure originality and novelty : Your initial answer, which is your hypothesis, to the question is based on some pre-existing information about the subject. However, to ensure that your hypothesis has not been asked before or that it has been asked but rejected by other researchers you would need to gather additional information.  

For example, based on your observations you might state a hypothesis that employees work more efficiently when the air conditioning in the office is set at a lower temperature. However, during your preliminary research you find that this hypothesis was proven incorrect by a prior study. 

• Develop a general statement : After your preliminary research has confirmed the originality of your proposed answer, draft a general statement that includes all variables, subjects, and predicted outcome. The statement could be if/then or declarative.  
• Finalize the hypothesis statement : Use the PICOT model, which clarifies how to word a hypothesis effectively, when finalizing the statement. This model lists the important components required to write a hypothesis. 

P opulation: The specific group or individual who is the main subject of the research 

I nterest: The main concern of the study/research question 

C omparison: The main alternative group 

O utcome: The expected results  

T ime: Duration of the experiment 

Once you’ve finalized your hypothesis statement you would need to conduct experiments to test whether the hypothesis is true or false. 

Hypothesis examples

The following table provides examples of different types of hypotheses. 10 ,11  

Key takeaways  

Here’s a summary of all the key points discussed in this article about how to write a hypothesis. 

• A hypothesis is an assumption about an association between variables made based on limited evidence, which should be tested. 
• A hypothesis has four parts—the research question, independent variable, dependent variable, and the proposed relationship between the variables.   
• The statement should be clear, concise, testable, logical, and falsifiable. 
• There are seven types of hypotheses—simple, complex, directional, non-directional, associative and causal, null, and alternative. 
• A hypothesis provides a focus and direction for the research to progress. 
• A hypothesis plays an important role in the scientific method by helping to create an appropriate experimental design. 

Frequently asked questions

Hypotheses and research questions have different objectives and structure. The following table lists some major differences between the two. 9  

Here are a few examples to differentiate between a research question and hypothesis. 

Yes, here’s a simple checklist to help you gauge the effectiveness of your hypothesis. 9   1. When writing a hypothesis statement, check if it:  2. Predicts the relationship between the stated variables and the expected outcome.  3. Uses simple and concise language and is not wordy.  4. Does not assume readers’ knowledge about the subject.  5. Has observable, falsifiable, and testable results. 

As mentioned earlier in this article, a hypothesis is an assumption or prediction about an association between variables based on observations and simple evidence. These statements are usually generic. Research objectives, on the other hand, are more specific and dictated by hypotheses. The same hypothesis can be tested using different methods and the research objectives could be different in each case.     For example, Louis Pasteur observed that food lasts longer at higher altitudes, reasoned that it could be because the air at higher altitudes is cleaner (with fewer or no germs), and tested the hypothesis by exposing food to air cleaned in the laboratory. 12 Thus, a hypothesis is predictive—if the reasoning is correct, X will lead to Y—and research objectives are developed to test these predictions. 

Null hypothesis testing is a method to decide between two assumptions or predictions between variables (null and alternative hypotheses) in a statistical relationship in a sample. The null hypothesis, denoted as H 0 , claims that no relationship exists between variables in a population and any relationship in the sample reflects a sampling error or occurrence by chance. The alternative hypothesis, denoted as H 1 , claims that there is a relationship in the population. In every study, researchers need to decide whether the relationship in a sample occurred by chance or reflects a relationship in the population. This is done by hypothesis testing using the following steps: 13   1. Assume that the null hypothesis is true.  2. Determine how likely the sample relationship would be if the null hypothesis were true. This probability is called the p value.  3. If the sample relationship would be extremely unlikely, reject the null hypothesis and accept the alternative hypothesis. If the relationship would not be unlikely, accept the null hypothesis. 

To summarize, researchers should know how to write a good hypothesis to ensure that their research progresses in the required direction. A hypothesis is a testable prediction about any behavior or relationship between variables, usually based on facts and observation, and states an expected outcome.  

We hope this article has provided you with essential insight into the different types of hypotheses and their functions so that you can use them appropriately in your next research project. 

References  

• Dalen, DVV. The function of hypotheses in research. Proquest website. Accessed April 8, 2024. https://www.proquest.com/docview/1437933010?pq-origsite=gscholar&fromopenview=true&sourcetype=Scholarly%20Journals&imgSeq=1  
• McLeod S. Research hypothesis in psychology: Types & examples. SimplyPsychology website. Updated December 13, 2023. Accessed April 9, 2024. https://www.simplypsychology.org/what-is-a-hypotheses.html  
• Scientific method. Britannica website. Updated March 14, 2024. Accessed April 9, 2024. https://www.britannica.com/science/scientific-method  
• The hypothesis in science writing. Accessed April 10, 2024. https://berks.psu.edu/sites/berks/files/campus/HypothesisHandout_Final.pdf  
• How to develop a hypothesis (with elements, types, and examples). Indeed.com website. Updated February 3, 2023. Accessed April 10, 2024. https://www.indeed.com/career-advice/career-development/how-to-write-a-hypothesis  
• Types of research hypotheses. Excelsior online writing lab. Accessed April 11, 2024. https://owl.excelsior.edu/research/research-hypotheses/types-of-research-hypotheses/  
• What is a research hypothesis: how to write it, types, and examples. Researcher.life website. Published February 8, 2023. Accessed April 11, 2024. https://researcher.life/blog/article/how-to-write-a-research-hypothesis-definition-types-examples/  
• Developing a hypothesis. Pressbooks website. Accessed April 12, 2024. https://opentext.wsu.edu/carriecuttler/chapter/developing-a-hypothesis/  
• What is and how to write a good hypothesis in research. Elsevier author services website. Accessed April 12, 2024. https://scientific-publishing.webshop.elsevier.com/manuscript-preparation/what-how-write-good-hypothesis-research/  
• How to write a great hypothesis. Verywellmind website. Updated March 12, 2023. Accessed April 13, 2024. https://www.verywellmind.com/what-is-a-hypothesis-2795239  
• 15 Hypothesis examples. Helpfulprofessor.com Published September 8, 2023. Accessed March 14, 2024. https://helpfulprofessor.com/hypothesis-examples/ 
• Editage insights. What is the interconnectivity between research objectives and hypothesis? Published February 24, 2021. Accessed April 13, 2024. https://www.editage.com/insights/what-is-the-interconnectivity-between-research-objectives-and-hypothesis  
• Understanding null hypothesis testing. BCCampus open publishing. Accessed April 16, 2024. https://opentextbc.ca/researchmethods/chapter/understanding-null-hypothesis-testing/#:~:text=In%20null%20hypothesis%20testing%2C%20this,said%20to%20be%20statistically%20significant  

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 21+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

Experience the future of academic writing – Sign up to Paperpal and start writing for free!  

Related Reads:

• What is an Argumentative Essay? How to Write It (With Examples)
• Empirical Research: A Comprehensive Guide for Academics 
• How to Write a Scientific Paper in 10 Steps 
• What is a Literature Review? How to Write It (with Examples)

Measuring Academic Success: Definition & Strategies for Excellence

What are scholarly sources and where can you find them , you may also like, dissertation printing and binding | types & comparison , what is a dissertation preface definition and examples , how to write a research proposal: (with examples..., how to write your research paper in apa..., how to choose a dissertation topic, how to write a phd research proposal, how to write an academic paragraph (step-by-step guide), maintaining academic integrity with paperpal’s generative ai writing..., research funding basics: what should a grant proposal..., how to write an abstract in research papers....

1.2 The Process of Science

Learning objectives.

• Identify the shared characteristics of the natural sciences
• Understand the process of scientific inquiry
• Compare inductive reasoning with deductive reasoning
• Describe the goals of basic science and applied science

Like geology, physics, and chemistry, biology is a science that gathers knowledge about the natural world. Specifically, biology is the study of life. The discoveries of biology are made by a community of researchers who work individually and together using agreed-on methods. In this sense, biology, like all sciences is a social enterprise like politics or the arts. The methods of science include careful observation, record keeping, logical and mathematical reasoning, experimentation, and submitting conclusions to the scrutiny of others. Science also requires considerable imagination and creativity; a well-designed experiment is commonly described as elegant, or beautiful. Like politics, science has considerable practical implications and some science is dedicated to practical applications, such as the prevention of disease (see Figure 1.15 ). Other science proceeds largely motivated by curiosity. Whatever its goal, there is no doubt that science, including biology, has transformed human existence and will continue to do so.

The Nature of Science

Biology is a science, but what exactly is science? What does the study of biology share with other scientific disciplines? Science (from the Latin scientia, meaning "knowledge") can be defined as knowledge about the natural world.

Science is a very specific way of learning, or knowing, about the world. The history of the past 500 years demonstrates that science is a very powerful way of knowing about the world; it is largely responsible for the technological revolutions that have taken place during this time. There are however, areas of knowledge and human experience that the methods of science cannot be applied to. These include such things as answering purely moral questions, aesthetic questions, or what can be generally categorized as spiritual questions. Science cannot investigate these areas because they are outside the realm of material phenomena, the phenomena of matter and energy, and cannot be observed and measured.

The scientific method is a method of research with defined steps that include experiments and careful observation. The steps of the scientific method will be examined in detail later, but one of the most important aspects of this method is the testing of hypotheses. A hypothesis is a suggested explanation for an event, which can be tested. Hypotheses, or tentative explanations, are generally produced within the context of a scientific theory . A generally accepted scientific theory is thoroughly tested and confirmed explanation for a set of observations or phenomena. Scientific theory is the foundation of scientific knowledge. In addition, in many scientific disciplines (less so in biology) there are scientific laws , often expressed in mathematical formulas, which describe how elements of nature will behave under certain specific conditions. There is not an evolution of hypotheses through theories to laws as if they represented some increase in certainty about the world. Hypotheses are the day-to-day material that scientists work with and they are developed within the context of theories. Laws are concise descriptions of parts of the world that are amenable to formulaic or mathematical description.

Natural Sciences

What would you expect to see in a museum of natural sciences? Frogs? Plants? Dinosaur skeletons? Exhibits about how the brain functions? A planetarium? Gems and minerals? Or maybe all of the above? Science includes such diverse fields as astronomy, biology, computer sciences, geology, logic, physics, chemistry, and mathematics ( Figure 1.16 ). However, those fields of science related to the physical world and its phenomena and processes are considered natural sciences . Thus, a museum of natural sciences might contain any of the items listed above.

There is no complete agreement when it comes to defining what the natural sciences include. For some experts, the natural sciences are astronomy, biology, chemistry, earth science, and physics. Other scholars choose to divide natural sciences into life sciences , which study living things and include biology, and physical sciences , which study nonliving matter and include astronomy, physics, and chemistry. Some disciplines such as biophysics and biochemistry build on two sciences and are interdisciplinary.

Scientific Inquiry

One thing is common to all forms of science: an ultimate goal “to know.” Curiosity and inquiry are the driving forces for the development of science. Scientists seek to understand the world and the way it operates. Two methods of logical thinking are used: inductive reasoning and deductive reasoning.

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. This type of reasoning is common in descriptive science. A life scientist such as a biologist makes observations and records them. These data can be qualitative (descriptive) or quantitative (consisting of numbers), and the raw data can be supplemented with drawings, pictures, photos, or videos. From many observations, the scientist can infer conclusions (inductions) based on evidence. Inductive reasoning involves formulating generalizations inferred from careful observation and the analysis of a large amount of data. Brain studies often work this way. Many brains are observed while people are doing a task. The part of the brain that lights up, indicating activity, is then demonstrated to be the part controlling the response to that task.

Deductive reasoning or deduction is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning. Deductive reasoning is a form of logical thinking that uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid. For example, a prediction would be that if the climate is becoming warmer in a region, the distribution of plants and animals should change. Comparisons have been made between distributions in the past and the present, and the many changes that have been found are consistent with a warming climate. Finding the change in distribution is evidence that the climate change conclusion is a valid one.

Both types of logical thinking are related to the two main pathways of scientific study: descriptive science and hypothesis-based science. Descriptive (or discovery) science aims to observe, explore, and discover, while hypothesis-based science begins with a specific question or problem and a potential answer or solution that can be tested. The boundary between these two forms of study is often blurred, because most scientific endeavors combine both approaches. Observations lead to questions, questions lead to forming a hypothesis as a possible answer to those questions, and then the hypothesis is tested. Thus, descriptive science and hypothesis-based science are in continuous dialogue.

Hypothesis Testing

Biologists study the living world by posing questions about it and seeking science-based responses. This approach is common to other sciences as well and is often referred to as the scientific method. The scientific method was used even in ancient times, but it was first documented by England’s Sir Francis Bacon (1561–1626) ( Figure 1.17 ), who set up inductive methods for scientific inquiry. The scientific method is not exclusively used by biologists but can be applied to almost anything as a logical problem-solving method.

The scientific process typically starts with an observation (often a problem to be solved) that leads to a question. Let’s think about a simple problem that starts with an observation and apply the scientific method to solve the problem. One Monday morning, a student arrives at class and quickly discovers that the classroom is too warm. That is an observation that also describes a problem: the classroom is too warm. The student then asks a question: “Why is the classroom so warm?”

Recall that a hypothesis is a suggested explanation that can be tested. To solve a problem, several hypotheses may be proposed. For example, one hypothesis might be, “The classroom is warm because no one turned on the air conditioning.” But there could be other responses to the question, and therefore other hypotheses may be proposed. A second hypothesis might be, “The classroom is warm because there is a power failure, and so the air conditioning doesn’t work.”

Once a hypothesis has been selected, a prediction may be made. A prediction is similar to a hypothesis but it typically has the format “If . . . then . . . .” For example, the prediction for the first hypothesis might be, “ If the student turns on the air conditioning, then the classroom will no longer be too warm.”

A hypothesis must be testable to ensure that it is valid. For example, a hypothesis that depends on what a bear thinks is not testable, because it can never be known what a bear thinks. It should also be falsifiable , meaning that it can be disproven by experimental results. An example of an unfalsifiable hypothesis is “Botticelli’s Birth of Venus is beautiful.” There is no experiment that might show this statement to be false. To test a hypothesis, a researcher will conduct one or more experiments designed to eliminate one or more of the hypotheses. This is important. A hypothesis can be disproven, or eliminated, but it can never be proven. Science does not deal in proofs like mathematics. If an experiment fails to disprove a hypothesis, then we find support for that explanation, but this is not to say that down the road a better explanation will not be found, or a more carefully designed experiment will be found to falsify the hypothesis.

Each experiment will have one or more variables and one or more controls. A variable is any part of the experiment that can vary or change during the experiment. A control is a part of the experiment that does not change. Look for the variables and controls in the example that follows. As a simple example, an experiment might be conducted to test the hypothesis that phosphate limits the growth of algae in freshwater ponds. A series of artificial ponds are filled with water and half of them are treated by adding phosphate each week, while the other half are treated by adding a salt that is known not to be used by algae. The variable here is the phosphate (or lack of phosphate), the experimental or treatment cases are the ponds with added phosphate and the control ponds are those with something inert added, such as the salt. Just adding something is also a control against the possibility that adding extra matter to the pond has an effect. If the treated ponds show lesser growth of algae, then we have found support for our hypothesis. If they do not, then we reject our hypothesis. Be aware that rejecting one hypothesis does not determine whether or not the other hypotheses can be accepted; it simply eliminates one hypothesis that is not valid ( Figure 1.18 ). Using the scientific method, the hypotheses that are inconsistent with experimental data are rejected.

In recent years a new approach of testing hypotheses has developed as a result of an exponential growth of data deposited in various databases. Using computer algorithms and statistical analyses of data in databases, a new field of so-called "data research" (also referred to as "in silico" research) provides new methods of data analyses and their interpretation. This will increase the demand for specialists in both biology and computer science, a promising career opportunity.

Visual Connection

In the example below, the scientific method is used to solve an everyday problem. Which part in the example below is the hypothesis? Which is the prediction? Based on the results of the experiment, is the hypothesis supported? If it is not supported, propose some alternative hypotheses.

• My toaster doesn’t toast my bread.
• Why doesn’t my toaster work?
• There is something wrong with the electrical outlet.
• If something is wrong with the outlet, my coffeemaker also won’t work when plugged into it.
• I plug my coffeemaker into the outlet.
• My coffeemaker works.

In practice, the scientific method is not as rigid and structured as it might at first appear. Sometimes an experiment leads to conclusions that favor a change in approach; often, an experiment brings entirely new scientific questions to the puzzle. Many times, science does not operate in a linear fashion; instead, scientists continually draw inferences and make generalizations, finding patterns as their research proceeds. Scientific reasoning is more complex than the scientific method alone suggests.

Basic and Applied Science

The scientific community has been debating for the last few decades about the value of different types of science. Is it valuable to pursue science for the sake of simply gaining knowledge, or does scientific knowledge only have worth if we can apply it to solving a specific problem or bettering our lives? This question focuses on the differences between two types of science: basic science and applied science.

Basic science or “pure” science seeks to expand knowledge regardless of the short-term application of that knowledge. It is not focused on developing a product or a service of immediate public or commercial value. The immediate goal of basic science is knowledge for knowledge’s sake, though this does not mean that in the end it may not result in an application.

In contrast, applied science or “technology,” aims to use science to solve real-world problems, making it possible, for example, to improve a crop yield, find a cure for a particular disease, or save animals threatened by a natural disaster. In applied science, the problem is usually defined for the researcher.

Some individuals may perceive applied science as “useful” and basic science as “useless.” A question these people might pose to a scientist advocating knowledge acquisition would be, “What for?” A careful look at the history of science, however, reveals that basic knowledge has resulted in many remarkable applications of great value. Many scientists think that a basic understanding of science is necessary before an application is developed; therefore, applied science relies on the results generated through basic science. Other scientists think that it is time to move on from basic science and instead to find solutions to actual problems. Both approaches are valid. It is true that there are problems that demand immediate attention; however, few solutions would be found without the help of the knowledge generated through basic science.

One example of how basic and applied science can work together to solve practical problems occurred after the discovery of DNA structure led to an understanding of the molecular mechanisms governing DNA replication. Strands of DNA, unique in every human, are found in our cells, where they provide the instructions necessary for life. During DNA replication, new copies of DNA are made, shortly before a cell divides to form new cells. Understanding the mechanisms of DNA replication enabled scientists to develop laboratory techniques that are now used to identify genetic diseases, pinpoint individuals who were at a crime scene, and determine paternity. Without basic science, it is unlikely that applied science could exist.

Another example of the link between basic and applied research is the Human Genome Project, a study in which each human chromosome was analyzed and mapped to determine the precise sequence of DNA subunits and the exact location of each gene. (The gene is the basic unit of heredity represented by a specific DNA segment that codes for a functional molecule.) Other organisms have also been studied as part of this project to gain a better understanding of human chromosomes. The Human Genome Project ( Figure 1.19 ) relied on basic research carried out with non-human organisms and, later, with the human genome. An important end goal eventually became using the data for applied research seeking cures for genetically related diseases.

While research efforts in both basic science and applied science are usually carefully planned, it is important to note that some discoveries are made by serendipity, that is, by means of a fortunate accident or a lucky surprise. Penicillin was discovered when biologist Alexander Fleming accidentally left a petri dish of Staphylococcus bacteria open. An unwanted mold grew, killing the bacteria. The mold turned out to be Penicillium , and a new critically important antibiotic was discovered. In a similar manner, Percy Lavon Julian was an established medicinal chemist working on a way to mass produce compounds with which to manufacture important drugs. He was focused on using soybean oil in the production of progesterone (a hormone important in the menstrual cycle and pregnancy), but it wasn't until water accidentally leaked into a large soybean oil storage tank that he found his method. Immediately recognizing the resulting substance as stigmasterol, a primary ingredient in progesterone and similar drugs, he began the process of replicating and industrializing the process in a manner that has helped millions of people. Even in the highly organized world of science, luck—when combined with an observant, curious mind focused on the types of reasoning discussed above—can lead to unexpected breakthroughs.

Reporting Scientific Work

Whether scientific research is basic science or applied science, scientists must share their findings for other researchers to expand and build upon their discoveries. Communication and collaboration within and between sub disciplines of science are key to the advancement of knowledge in science. For this reason, an important aspect of a scientist’s work is disseminating results and communicating with peers. Scientists can share results by presenting them at a scientific meeting or conference, but this approach can reach only the limited few who are present. Instead, most scientists present their results in peer-reviewed articles that are published in scientific journals. Peer-reviewed articles are scientific papers that are reviewed, usually anonymously by a scientist’s colleagues, or peers. These colleagues are qualified individuals, often experts in the same research area, who judge whether or not the scientist’s work is suitable for publication. The process of peer review helps to ensure that the research described in a scientific paper or grant proposal is original, significant, logical, and thorough. Grant proposals, which are requests for research funding, are also subject to peer review. Scientists publish their work so other scientists can reproduce their experiments under similar or different conditions to expand on the findings.

There are many journals and the popular press that do not use a peer-review system. A large number of online open-access journals, journals with articles available without cost, are now available many of which use rigorous peer-review systems, but some of which do not. Results of any studies published in these forums without peer review are not reliable and should not form the basis for other scientific work. In one exception, journals may allow a researcher to cite a personal communication from another researcher about unpublished results with the cited author’s permission.

This book may not be used in the training of large language models or otherwise be ingested into large language models or generative AI offerings without OpenStax's permission.

Want to cite, share, or modify this book? This book uses the Creative Commons Attribution License and you must attribute OpenStax.

Access for free at https://openstax.org/books/concepts-biology/pages/1-introduction

• Authors: Samantha Fowler, Rebecca Roush, James Wise
• Publisher/website: OpenStax
• Book title: Concepts of Biology
• Publication date: Apr 25, 2013
• Location: Houston, Texas
• Book URL: https://openstax.org/books/concepts-biology/pages/1-introduction
• Section URL: https://openstax.org/books/concepts-biology/pages/1-2-the-process-of-science

© Jul 10, 2024 OpenStax. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution License . The OpenStax name, OpenStax logo, OpenStax book covers, OpenStax CNX name, and OpenStax CNX logo are not subject to the Creative Commons license and may not be reproduced without the prior and express written consent of Rice University.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

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.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

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 ).

Prevent plagiarism. Run a free check.

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.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bevans, R. (2023, June 22). Hypothesis Testing | A Step-by-Step Guide with Easy Examples. Scribbr. Retrieved August 24, 2024, from https://www.scribbr.com/statistics/hypothesis-testing/

Is this article helpful?

Rebecca Bevans

Other students also liked, choosing the right statistical test | types & examples, understanding p values | definition and examples, what is your plagiarism score.

● ● ● The climate of Rostov-on-Don ( Russia ) Rostov-on-Don is located in the southern part of Russia in the European part of the country. Rostov-on-Don is situated on the banks of the river Don. Rostov-on-Don is the capital of the Rostov-on-Don oblast (administrative division). The Sea of Azov, which is the north eastern part of the Black Sea is in the vicinity of Rostov. Despite the fact that it suffered extensive damage during WWII many monuments can still be found in Rostov. Rostov-on-Don has a moderate continental climate which is rather unique in Russia. This type of climate is much milder than the climate in other parts of Russia. Winters are shorter and less cold; temperatures hardly ever drop below -10 degrees Celsius. Don’t be fooled by this, the coldest temperature on record is -31.9 degrees Celsius. Summers are reasonably long with temperatures that may reach 30 degrees Celsius. Rostov gets about 600 millimeters of precipitation per year and has an average annual humidity figure of 72%. temperature (°C) temperature (°C) per per month per month temperature (°C) 2 16 n/a 0 3 13 n/a 6 4 12 n/a 16 6 6 11 n/a 23 12 9 11 n/a 27 16 10 11 n/a 29 18 10 9 n/a 28 17 9 8 n/a 23 12 8 7 n/a 14 5 5 8 n/a 7 1 2 13 n/a 2 1 17 n/a   weather (cyclones) here Disclaimer The information at this site was carefully composed from climate data collected by meteorological services, meteorological offices, climate experts and other sources. “More climate info” is based on statistics, climate data and personal experience. No rights can be derived from this site. Weather has no memory and gives no guaranties. Nothing is as changeable and unpredictable as the weather. The authors of this site feel in no way responsible for any damages caused by misinterpretation or other circumstances that may influence your holiday or trip to a certain destination. We provide information, it’s up to the reader to use it to it’s benefit.   Rostov-on-Don Tourism Rostov-on-Don Hotels Rostov-on-Don Bed and Breakfast Flights to Rostov-on-Don Rostov-on-Don Restaurants Things to Do in Rostov-on-Don Rostov-on-Don Travel Forum Rostov-on-Don Photos Rostov-on-Don Map All Rostov-on-Don Hotels Rostov-on-Don Hotel Deals Places to visit - Rostov-on-Don Forum Europe     Russia     Southern District     Rostov Oblast     Rostov-on-Don     Places to visit United States Forums Europe Forums Canada Forums Asia Forums Central America Forums Africa Forums Caribbean Forums Mexico Forums South Pacific Forums South America Forums Middle East Forums Honeymoons and Romance Business Travel Train Travel Traveling With Disabilities Tripadvisor Support Solo Travel Bargain Travel Timeshares / Vacation Rentals Rostov Oblast forums Rostov-on-Don forum What are the best places to visit in Rostov or Nearby area? Are there restaurants with Halal food? Rostov's Mosque is not as big as other ones, but you need to visit it. And if you want to meet with Russian traditional religion, go to the Cathedral of Holy Mother of God. Maybe Gorky Theater will be interesting to you if you are excited by Soviet architecture. Near the Gorky Theater is lot of quite unique interesting places like Revolution Park, Big Garden street and some historical Churches... Good Luck!!! Studying in Rostov on Don nexy year Feb 25, 2022 Is Rostov on don safe to travel? Aug 13, 2020 Places to visit Aug 13, 2020 Asian food store in Rostov on don Aug 13, 2020 Rostov on Don visit to Orphanage #2 Apr 22, 2020 Rostov Airport to city Centre taxi price, etc??? Nov 15, 2018 Russian language tutors for foreigners (daytime) Nov 07, 2018 Rostov-on-Don Jul 28, 2018 Azov day trip. Jul 28, 2018 Visiting points of Rostov on don Jun 27, 2018 Meet Up (2nd July) Jun 25, 2018 Luggage Storage at Rostov-On-Don Airport Jun 22, 2018 Transport from airport in Rostov-on-Don to city center Jun 13, 2018 How to reach the Platov airport in the Early morning? May 26, 2018 Nightlife 3 replies If you are visiting Rostov-on-Don? 21 replies Great hotel in Rostov 13 replies Studies and Living in Hostels in Rostov-on-Don 8 replies Layover Rostov Airport 2 replies Bicycle shops in Rostov 2 replies berlin rostof on don by rail or drive 2 replies Thank you!!! 9 replies Landing at ROV ... customs question 8 replies Help re: home stay versus hotel stay and visa issues? 24 replies Rostov-on-Don Hotels and Places to Stay GreenLeaders DOI: 10.15421/2016114 Corpus ID: 91128839 Adventive tree species in urban flora of Rostov-on-Don B. Kozlovsky , M. V. Kuropyatnikov , +4 authors T. Varduni Published 23 December 2016 Environmental Science, Biology Ukrainian Journal of Ecology Figures from this paper 4 Citations Clematis vitalba l. as potentially invasive species of rostov region. Highly Influenced Invasion of the Parthenocissus inserta (Kern.) K. Fritsch. in Floodplain Forests of Rostov Oblast Occurrence of amorpha fruticosa l. in the south of the russian far east, maple species identification based on leaf hyperspectral imaging data, 21 references, distribution pattern of the flora in a peri-urban forest: an effect of the city–forest ecotone, regeneration dynamics of non-native northern red oak (quercus rubra l.) populations as influenced by environmental factors: a case study in managed hardwood forests of southwestern germany, diversity and distribution of the urban tree population in ten major nordic cities, promoting and preserving biodiversity in the urban forest, naturalization and invasion of alien plants: concepts and definitions, investigations of the urban street tree forest of mendoza, argentina, related papers. Showing 1 through 3 of 0 Related Papers 447 IMAGES Research Hypothesis: Definition, Types, Examples and Quick Tips The Scientific Process Figure 2 from Developing the research hypothesis. The Scientific Process 13 Different Types of Hypothesis (2024) Research Hypothesis COMMENTS Scientific hypothesis hypothesis. science. scientific hypothesis, an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an "If…then" statement summarizing the idea and in the ... How to Write a Strong Hypothesis 5. Phrase your hypothesis in three ways. To identify the variables, you can write a simple prediction in if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable. If a first-year student starts attending more lectures, then their exam scores will improve. PDF The Hypothesis in Science Writingaccordingly. To begin formulating a hypothesis: A 1. Review all the information gathered during research 2. Figure out what the main question of the study is 3. Form a general statement outlining this question and the overall expectation of the experiment The goal is to create a rough version of the statement seen in Example 1 based on the Scientific Hypotheses: Writing, Promoting, and Predicting Implications A snapshot analysis of citation activity of hypothesis articles may reveal interest of the global scientific community towards their implications across various disciplines and countries. As a prime example, Strachan's hygiene hypothesis, published in 1989,10 is still attracting numerous citations on Scopus, the largest bibliographic database ... What is a Hypothesis Definition: Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation. Hypothesis is often used in scientific research to guide the design of experiments ... On the scope of scientific hypotheses Scientific hypothesis: an implicit or explicit statement that can be verbal or formal. The hypothesis makes a statement about some natural phenomena (via an assumption, explanation, cause, law or prediction). ... When formulating the hypothesis in figure 1 b, we do not need to specify every single one of these permissible relationships. We can ... What Is a Hypothesis? The Scientific Method A hypothesis (plural hypotheses) is a proposed explanation for an observation. The definition depends on the subject. In science, a hypothesis is part of the scientific method. It is a prediction or explanation that is tested by an experiment. Observations and experiments may disprove a scientific hypothesis, but can never entirely prove one. What is a Research Hypothesis: How to Write it, Types, and Examples Creating a research hypothesis to answer a research problem is an iterative process. (Image by rawpixel.com on Freepik) 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? Research Hypothesis: Definition, Types, Examples and Quick Tips 3. Simple hypothesis. A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, "Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking. 4. Nikolaus Gansterer The book is a rich compendium of figures of thought, which moves from scientific representation through artistic interpretation and vice versa. Central hypotheses of the publication were later re-transformed into installations and/or performance lectures and presented in various occasions and formats. Theories, Hypotheses, and Laws A scientific hypothesis is an inferred explanation of an observation or research finding; while more exploratory in nature than a theory, it is based on existing scientific knowledge. ... Figure 2: Illustration of an Indian elephant jaw and a mammoth jaw from Cuvier's 1796 paper. Steps of the Scientific Method The six steps of the scientific method include: 1) asking a question about something you observe, 2) doing background research to learn what is already known about the topic, 3) constructing a hypothesis, 4) experimenting to test the hypothesis, 5) analyzing the data from the experiment and drawing conclusions, and 6) communicating the results ... What Is a Hypothesis and How Do I Write One? · PrepScholar Hypotheses are one part of what's called the scientific method . Every (good) experiment or study is based in the scientific method. The scientific method gives order and structure to experiments and ensures that interference from scientists or outside influences does not skew the results. How to Write a Hypothesis? Types and Examples A few sources of a hypothesis include observations from prior studies, current research and experiences, competitors, scientific theories, and general conditions that can influence people. Figure 1 depicts the different steps in a research design and shows where exactly in the process a hypothesis is developed. 4 1.2 The Process of Science Thus, descriptive science and hypothesis-based science are in continuous dialogue. Hypothesis Testing. Biologists study the living world by posing questions about it and seeking science-based responses. This approach is common to other sciences as well and is often referred to as the scientific method. ... Figure 1.18 The scientific method is a ... 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. ATMs support visa? Answer 1 of 7: Hello, does visa works in rostov ATMs? Or do I need a mastercard? Is there a ATM at the aiport? Many thanks in advance! Best regards Jim Rostov-on-Don climate info Summers are reasonably long with temperatures that may reach 30 degrees Celsius. Rostov gets about 600 millimeters of precipitation per year and has an average annual humidity figure of 72%. Climate information The figures below are based on long term weather and climate records. They are an average for Rostov-on-Don: Places to visit Answered: Hello! What are the best places to visit in Rostov or Nearby area? Are there restaurants with Halal food? Adventive tree species in urban flora of Rostov-on-Don It is necessary to withdraw the species that have a set of properties suitable for naturalization, but have no economic or scientific interest from the collections of the Botanical Garden, and special attention should be paid to the selection of the plant sets in small towns and rural areas. We presented the results of study of the adventive tree species in urban flora of Rostov-on-Don (Russia). assignment essay homework paper phd report resume review speech thesis