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What is a Hypothesis – Types, Examples and Writing Guide

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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

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Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

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Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

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

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

Some key points about hypotheses:

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

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

Types of Research Hypotheses

Alternative hypothesis.

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

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

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

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

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

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

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

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

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

Null Hypothesis

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

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

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

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

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

Nondirectional Hypothesis

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

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

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

Directional Hypothesis

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

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

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

Falsifiability

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

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

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

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

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

Can a Hypothesis be Proven?

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

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

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

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

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

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

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

How to Write a Hypothesis

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

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

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

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

More Examples

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

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

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

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

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

Research Hypothesis 101

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

What is a hypothesis?

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

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

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

Hypothesis: sleep impacts academic performance.

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

But that’s not good enough…

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

What is a research hypothesis?

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

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

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

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

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

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

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

Hypothesis Essential #2: Testability (Provability)

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

For example, consider the hypothesis we mentioned earlier:

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

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

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

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

Defining A Research Hypothesis

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

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

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

What about the null hypothesis?

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

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

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

And there you have it – hypotheses in a nutshell. 

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

Psst... there’s more!

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

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16 Comments

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

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

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

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

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

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

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

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

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

could you please elaborate it more

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

This is very helpful

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

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

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

this is very important note help me much more

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Definition of hypothesis

Did you know.

The Difference Between Hypothesis and Theory

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

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

A hypothesis is usually tentative; it's an assumption or suggestion made strictly for the objective of being tested.

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

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch, with theory being the more common choice.

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

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

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

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

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

• proposition
• supposition

hypothesis , theory , law mean a formula derived by inference from scientific data that explains a principle operating in nature.

hypothesis implies insufficient evidence to provide more than a tentative explanation.

theory implies a greater range of evidence and greater likelihood of truth.

law implies a statement of order and relation in nature that has been found to be invariable under the same conditions.

Examples of hypothesis in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'hypothesis.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Greek, from hypotithenai to put under, suppose, from hypo- + tithenai to put — more at do

1641, in the meaning defined at sense 1a

Phrases Containing hypothesis

• counter - hypothesis
• nebular hypothesis
• null hypothesis
• planetesimal hypothesis
• Whorfian hypothesis

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Cite this Entry

“Hypothesis.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/hypothesis. Accessed 11 May. 2024.

Kids Definition

Kids definition of hypothesis, medical definition, medical definition of hypothesis, more from merriam-webster on hypothesis.

Nglish: Translation of hypothesis for Spanish Speakers

Britannica English: Translation of hypothesis for Arabic Speakers

Britannica.com: Encyclopedia article about hypothesis

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What is a scientific hypothesis?

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

Hypothesis basics

What makes a hypothesis testable.

• Types of hypotheses
• Hypothesis versus theory

Additional resources

Bibliography.

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

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

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

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

Here are some examples of hypothesis statements:

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

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

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

Types of scientific hypotheses

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

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

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

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

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

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

Scientific theory vs. scientific hypothesis

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

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

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

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

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

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

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

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

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

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

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

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

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Definition of a Hypothesis

What it is and how it's used in sociology

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A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence.

Within social science, a hypothesis can take two forms. It can predict that there is no relationship between two variables, in which case it is a null hypothesis . Or, it can predict the existence of a relationship between variables, which is known as an alternative hypothesis.

In either case, the variable that is thought to either affect or not affect the outcome is known as the independent variable, and the variable that is thought to either be affected or not is the dependent variable.

Researchers seek to determine whether or not their hypothesis, or hypotheses if they have more than one, will prove true. Sometimes they do, and sometimes they do not. Either way, the research is considered successful if one can conclude whether or not a hypothesis is true. 

Null Hypothesis

A researcher has a null hypothesis when she or he believes, based on theory and existing scientific evidence, that there will not be a relationship between two variables. For example, when examining what factors influence a person's highest level of education within the U.S., a researcher might expect that place of birth, number of siblings, and religion would not have an impact on the level of education. This would mean the researcher has stated three null hypotheses.

Alternative Hypothesis

Taking the same example, a researcher might expect that the economic class and educational attainment of one's parents, and the race of the person in question are likely to have an effect on one's educational attainment. Existing evidence and social theories that recognize the connections between wealth and cultural resources , and how race affects access to rights and resources in the U.S. , would suggest that both economic class and educational attainment of the one's parents would have a positive effect on educational attainment. In this case, economic class and educational attainment of one's parents are independent variables, and one's educational attainment is the dependent variable—it is hypothesized to be dependent on the other two.

Conversely, an informed researcher would expect that being a race other than white in the U.S. is likely to have a negative impact on a person's educational attainment. This would be characterized as a negative relationship, wherein being a person of color has a negative effect on one's educational attainment. In reality, this hypothesis proves true, with the exception of Asian Americans , who go to college at a higher rate than whites do. However, Blacks and Hispanics and Latinos are far less likely than whites and Asian Americans to go to college.

Formulating a Hypothesis

Formulating a hypothesis can take place at the very beginning of a research project , or after a bit of research has already been done. Sometimes a researcher knows right from the start which variables she is interested in studying, and she may already have a hunch about their relationships. Other times, a researcher may have an interest in ​a particular topic, trend, or phenomenon, but he may not know enough about it to identify variables or formulate a hypothesis.

Whenever a hypothesis is formulated, the most important thing is to be precise about what one's variables are, what the nature of the relationship between them might be, and how one can go about conducting a study of them.

Updated by Nicki Lisa Cole, Ph.D

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Course: biology library   >   unit 1, the scientific method.

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• The scientific method and experimental design

Introduction

• Make an observation.
• Ask a question.
• Form a hypothesis , or testable explanation.
• Make a prediction based on the hypothesis.
• Test the prediction.
• Iterate: use the results to make new hypotheses or predictions.

Scientific method example: Failure to toast

1. make an observation..

• Observation: the toaster won't toast.

2. Ask a question.

• Question: Why won't my toaster toast?

3. Propose a hypothesis.

• Hypothesis: Maybe the outlet is broken.

4. Make predictions.

• Prediction: If I plug the toaster into a different outlet, then it will toast the bread.

5. Test the predictions.

• Test of prediction: Plug the toaster into a different outlet and try again.
• If the toaster does toast, then the hypothesis is supported—likely correct.
• If the toaster doesn't toast, then the hypothesis is not supported—likely wrong.

Logical possibility

Practical possibility, building a body of evidence, 6. iterate..

• Iteration time!
• If the hypothesis was supported, we might do additional tests to confirm it, or revise it to be more specific. For instance, we might investigate why the outlet is broken.
• If the hypothesis was not supported, we would come up with a new hypothesis. For instance, the next hypothesis might be that there's a broken wire in the toaster.

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

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

What is Hypothesis?

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

Hypothesis Meaning

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

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

Characteristics of Hypothesis

Here are some key characteristics of a hypothesis:

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

Sources of Hypothesis

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

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

Types of Hypothesis

Here are some common types of hypotheses:

Simple Hypothesis

Complex hypothesis, directional hypothesis.

• Non-directional Hypothesis

Null Hypothesis (H0)

Alternative hypothesis (h1 or ha), statistical hypothesis, research hypothesis, associative hypothesis, causal hypothesis.

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

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

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

Non-Directional Hypothesis

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

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

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

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

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

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

Causal Hypothesis are different from other ideas because they say that one thing causes another. This means there’s a cause and effect relationship between variables involved in the situation. They say that when one thing changes, it directly makes another thing change.

Hypothesis Examples

Following are the examples of hypotheses based on their types:

Simple Hypothesis Example

• Studying more can help you do better on tests.
• Getting more sun makes people have higher amounts of vitamin D.

Complex Hypothesis Example

• How rich you are, how easy it is to get education and healthcare greatly affects the number of years people live.
• A new medicine’s success relies on the amount used, how old a person is who takes it and their genes.

Directional Hypothesis Example

• Drinking more sweet drinks is linked to a higher body weight score.
• Too much stress makes people less productive at work.

Non-directional Hypothesis Example

• Drinking caffeine can affect how well you sleep.
• People often like different kinds of music based on their gender.
• The average test scores of Group A and Group B are not much different.
• There is no connection between using a certain fertilizer and how much it helps crops grow.

Alternative Hypothesis (Ha)

• Patients on Diet A have much different cholesterol levels than those following Diet B.
• Exposure to a certain type of light can change how plants grow compared to normal sunlight.
• The average smarts score of kids in a certain school area is 100.
• The usual time it takes to finish a job using Method A is the same as with Method B.
• Having more kids go to early learning classes helps them do better in school when they get older.
• Using specific ways of talking affects how much customers get involved in marketing activities.
• Regular exercise helps to lower the chances of heart disease.
• Going to school more can help people make more money.
• Playing violent video games makes teens more likely to act aggressively.
• Less clean air directly impacts breathing health in city populations.

Functions of Hypothesis

Hypotheses have many important jobs in the process of scientific research. Here are the key functions of hypotheses:

• Guiding Research: Hypotheses give a clear and exact way for research. They act like guides, showing the predicted connections or results that scientists want to study.
• Formulating Research Questions: Research questions often create guesses. They assist in changing big questions into particular, checkable things. They guide what the study should be focused on.
• Setting Clear Objectives: Hypotheses set the goals of a study by saying what connections between variables should be found. They set the targets that scientists try to reach with their studies.
• Testing Predictions: Theories guess what will happen in experiments or observations. By doing tests in a planned way, scientists can check if what they see matches the guesses made by their ideas.
• Providing Structure: Theories give structure to the study process by arranging thoughts and ideas. They aid scientists in thinking about connections between things and plan experiments to match.
• Focusing Investigations: Hypotheses help scientists focus on certain parts of their study question by clearly saying what they expect links or results to be. This focus makes the study work better.
• Facilitating Communication: Theories help scientists talk to each other effectively. Clearly made guesses help scientists to tell others what they plan, how they will do it and the results expected. This explains things well with colleagues in a wide range of audiences.
• Generating Testable Statements: A good guess can be checked, which means it can be looked at carefully or tested by doing experiments. This feature makes sure that guesses add to the real information used in science knowledge.
• Promoting Objectivity: Guesses give a clear reason for study that helps guide the process while reducing personal bias. They motivate scientists to use facts and data as proofs or disprovals for their proposed answers.
• Driving Scientific Progress: Making, trying out and adjusting ideas is a cycle. Even if a guess is proven right or wrong, the information learned helps to grow knowledge in one specific area.

How Hypothesis help in Scientific Research?

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

• Initiating Investigations: Hypotheses are the beginning of science research. They come from watching, knowing what’s already known or asking questions. This makes scientists make certain explanations that need to be checked with tests.
• Formulating Research Questions: Ideas usually come from bigger questions in study. They help scientists make these questions more exact and testable, guiding the study’s main point.
• Setting Clear Objectives: Hypotheses set the goals of a study by stating what we think will happen between different things. They set the goals that scientists want to reach by doing their studies.
• Designing Experiments and Studies: Assumptions help plan experiments and watchful studies. They assist scientists in knowing what factors to measure, the techniques they will use and gather data for a proposed reason.
• Testing Predictions: Ideas guess what will happen in experiments or observations. By checking these guesses carefully, scientists can see if the seen results match up with what was predicted in each hypothesis.
• Analysis and Interpretation of Data: Hypotheses give us a way to study and make sense of information. Researchers look at what they found and see if it matches the guesses made in their theories. They decide if the proof backs up or disagrees with these suggested reasons why things are happening as expected.
• Encouraging Objectivity: Hypotheses help make things fair by making sure scientists use facts and information to either agree or disagree with their suggested reasons. They lessen personal preferences by needing proof from experience.
• Iterative Process: People either agree or disagree with guesses, but they still help the ongoing process of science. Findings from testing ideas make us ask new questions, improve those ideas and do more tests. It keeps going on in the work of science to keep learning things.

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Summary – Hypothesis

A hypothesis is a testable statement serving as an initial explanation for phenomena, based on observations, theories, or existing knowledge. It acts as a guiding light for scientific research, proposing potential relationships between variables that can be empirically tested through experiments and observations. The hypothesis must be specific, testable, falsifiable, and grounded in prior research or observation, laying out a predictive, if-then scenario that details a cause-and-effect relationship. It originates from various sources including existing theories, observations, previous research, and even personal curiosity, leading to different types, such as simple, complex, directional, non-directional, null, and alternative hypotheses, each serving distinct roles in research methodology. The hypothesis not only guides the research process by shaping objectives and designing experiments but also facilitates objective analysis and interpretation of data, ultimately driving scientific progress through a cycle of testing, validation, and refinement.

FAQs on Hypothesis

What is a hypothesis.

A guess is a possible explanation or forecast that can be checked by doing research and experiments.

What are Components of a Hypothesis?

The components of a Hypothesis are Independent Variable, Dependent Variable, Relationship between Variables, Directionality etc.

What makes a Good Hypothesis?

Testability, Falsifiability, Clarity and Precision, Relevance are some parameters that makes a Good Hypothesis

Can a Hypothesis be Proven True?

You cannot prove conclusively that most hypotheses are true because it’s generally impossible to examine all possible cases for exceptions that would disprove them.

How are Hypotheses Tested?

Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data

Can Hypotheses change during Research?

Yes, you can change or improve your ideas based on new information discovered during the research process.

What is the Role of a Hypothesis in Scientific Research?

Hypotheses are used to support scientific research and bring about advancements in knowledge.

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What is Hypothesis?

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

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

Characteristics of Hypothesis

Following are the characteristics of the hypothesis:

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

Sources of Hypothesis

Following are the sources of hypothesis:

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

Types of Hypothesis

There are six forms of hypothesis and they are:

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

Simple Hypothesis

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

Complex Hypothesis

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

Directional Hypothesis

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

Non-directional Hypothesis

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

Null Hypothesis

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

Associative and Causal Hypothesis

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

Examples of Hypothesis

Following are the examples of hypotheses based on their types:

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

Functions of Hypothesis

Following are the functions performed by the hypothesis:

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

How will Hypothesis help in the Scientific Method?

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

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

Frequently Asked Questions – FAQs

What is hypothesis.

A hypothesis is an assumption made based on some evidence.

Give an example of simple hypothesis?

What are the types of hypothesis.

Types of hypothesis are:

• Associative and Casual hypothesis

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

Define complex hypothesis..

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

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Lock-and-key model

strong>Lock-and-key model n., [lɑk ænd ki ˈmɑdl̩] Definition: a model for enzyme-substrate interaction

Table of Contents

Lock-and-key model Definition

Lock-and-key model is a model for enzyme-substrate interaction suggesting that the enzyme and the substrate possess specific complementary geometric shapes that fit exactly into one another. In this model, enzymes are depicted as highly specific. They must bind to specific substrates before they catalyze chemical reactions . The term is a pivotal concept in enzymology to elucidate the intricate interaction between enzymes and substrates at the molecular level. In the lock-and-key model, the enzyme-substrate interaction suggests that the enzyme and the substrate possess specific complementary geometric shapes that fit exactly into one another. Like a key  into a  lock , only the correct size and shape of the substrate ( the key ) would fit into the  active site  ( the keyhole ) of the enzyme ( the lock ).

Compare: Induced fit model   See also: enzyme , active site , substrate

Lock-and-key vs. Induced Fit Model

At present, two models attempt to explain enzyme-substrate specificity; one of which is the lock-and-key model , and the other is the Induced fit model . The lock and key model theory was first postulated by  Emil Fischer   in 1894. The lock-and-key enzyme action proposes the high specificity of enzymes. However, it does not explain the stabilization of the transition state that the enzymes achieve. The induced fit model (proposed by Daniel Koshland in 1958) suggests that the active site continues to change until the substrate is completely bound to the active site of the enzyme, at which point the final shape and charge are determined. Unlike the lock-and-key model, the induced fit model shows that enzymes are rather flexible structures. Nevertheless, Fischer’s Lock and Key theory laid an important foundation for subsequent research, such as during the refinement of the enzyme-substrate complex mechanism, as ascribed in the induced fit model. The lock-and-key hypothesis has opened ideas where enzyme action is not merely catalytic but incorporates a rather complex process in how they interact with the correct substrates with precision.

Key Components

Components of the lock and key model:

• Enzyme : the enzyme structure is a three-dimensional protein configuration, with an active site from where the substrate binds.
• Substrate : often an organic molecule, a substrate possesses a structural feature that complements the geometry of the enzyme’s active site.

In the lock and key model, both the enzymes and the substrates facilitate the formation of a complex that lowers the activation energy needed for a chemical transformation to occur. Such reduction in the activation energy allows the chemical reaction to proceed at a relatively faster rate, making enzymes crucial in various biological and molecular processes.

Lock-and-key Model Examples

Some of the common examples that are often discussed in the context of the Lock and Key Model are as follows:

• Enzyme lactate dehydrogenase with a specific active site for its substrates, pyruvate and lactate. The complex facilitates the interconversion of pyruvate and lactate during anaerobic respiration
• Enzyme carbonic anhydrase with a specific active site for the substrates carbon dioxide and water. The complex facilitates the hydration of carbon dioxide, forming bicarbonate
• Enzyme lysozyme binding with a bacterial cell wall peptidoglycan, which is a vital immune function

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• Aryal, S. and Karki, P. (2023).  “Lock and Key Model- Mode of Action of Enzymes”. Microbenotes.com. https://microbenotes.com/lock-and-key-model-mode-of-action-of-enzymes/
• Farhana, A., & Lappin, S. L. (2023, May).  Biochemistry, Lactate Dehydrogenase . Nih.gov; StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK557536/

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

Last updated on January 11th, 2024

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Conceptualising care: critical perspectives on informal care and inequality

• Original Article
• Open access
• Published: 15 December 2023
• Volume 22 , pages 53–70, ( 2024 )

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• Michelle Peterie 1 &
• Alex Broom 1  

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Informal care occupies a paradoxical place in contemporary societies. It is at once reified as an inherent social good, and minimised, devalued, and pushed to the margins. The current ‘care crisis’ is bringing these tensions into sharp relief, fuelling renewed interest in care and its absences across a wide range of disciplines. In this article, we present an overview of five key literatures for comprehending informal care, with a focus on issues of inequality and injustice. These bodies of scholarship—which, respectively, emphasise the political-economic, affective, policy, geographic, and ecological dimensions of informal care—together furnish a critical conceptualisation of informal care that both recognises care’s social value, and underlines its embeddedness in systems and structures of oppression. Informal care, we show, evades easy definition, requiring a sophisticated array of critical concepts to capture its everyday complexities, avoid reductionism, and ultimately enable individual and collective flourishing.

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Introduction

In the popular imaginary, informal care is frequently romanticised. Providing unpaid care to another—a child, a spouse, a parent, a stranger—is applauded as a virtuous and deeply human action. Yet, this work of care is also sanitised, minimised, and pushed to the margins. As the COVID-19 pandemic has revealed, the day-to-day realities of unpaid care provision frequently jar with the rose-tinted ideal (e.g. Muldrew et al. 2021 ; Wagenaar and Prainsack 2021 ; Tunstall 2022 ; Lloyd 2023 ). Within the social sciences, growing recognition of this discord and of an accelerating ‘care crisis’ (e.g. Dowling 2021 ; Fine 2012 ; Williams 2018 ) is fuelling renewed scholarly interest in the intimate economies and relationalities of (formal and informal) care. Much of this scholarship—diverse and multidisciplinary as it is—advances a more critical conception of care as both an intuitive locus of social value, and a sphere of profound (though not immutable) inequality and injustice.

Care, in its broadest sense, is often taken to refer to “physical and emotional labour” (Conradson 2003 , p. 451) carried out on behalf of another. Within such ‘commonsense’ definitions, further delineation is typically made between ‘formal’ and ‘informal’ care—the former referring to acts of care carried out in the context of paid employment, and the latter to acts of care undertaken without formal recompense, often in the context of existing social relationships. In both contexts, caring action is generally assumed to be animated and underpinned by caring feelings , whether these arise from the moral disposition of the carer themselves or from the specificities of the carer’s relationship with the intended beneficiaries of their actions. As thus understood, care encompasses both physical acts of rendering assistance (‘caring for’) and internal feelings of concern or commitment (‘caring about’) (Bowlby 2012 ). As an ideal type, care melds both action and sentiment.

Despite the apparent straightforwardness of such conceptions, theorising care—and understanding the contemporary contours of informal care specifically—is a delicate proposition. As a plethora of social science scholars have argued (e.g. Bom and Stockel 2021 ; Bubeck 1995 ; Clement 1996 ; Gordon et al. 1996 ; Goodin 1985 ; Held 2005 ; Kittay 1999 ; Tronto 1998 ; Lynch 2022 ; Mol 2009 ; Fine 2007 ), popular understandings of care gloss over many of its lived complexities. Conceptualisations such as those offered above, partnering action and sentiment, frequently fail to account for dimensions such as care’s exploitative and coercive potential (Williams 2018 ); the possibility of fragile, fraught, or absence of emotional intimacies (Broom et al. 2016 ); the existence of relational interdependencies and entanglements that collapse simple binaries between ‘carers’ and ‘care recipients’ (Siira et al. 2019 ; Molyneaux et al. 2010 ); and the difficulties sometimes associated with delineating ‘formal’ and ‘informal’ care (Fine 2007 ; Glucksmann 2005 ). They also obscure the unpaid care work performed for (and potentially by ) non-human animals and environments (Barca 2020 ).

Our aim in this article is therefore to present an enhanced theoretical scaffolding for informal care, which captures its evolving and enduring nuances. While it is not our intention to develop or advance any one definition of informal care, we begin (in the interests of pragmatism) with a working definition of informal care, adapted from Bernice Fisher and Joan Tronto’s ( 1990 ) definition of care more broadly. Informal care, as thus defined, is an unpaid activity “that includes everything we do to maintain, continue, and repair our 'world' so that we can live in it as well as possible. That world includes our bodies, ourselves, and our environment” (Fisher and Tronto 1990 , p. 40). This definition encompasses the intensive and multidimensional labour involved in raising a child or caring for a loved-one at the end of life, but also allows room for the (less recognisable) forms of care involved in advocating for a stranger, being a friend, nourishing one’s own body, or working to revegetate a threatened habitat. Using this definition as a pragmatic jumping off point, the remainder of this article will explore key theoretical resources from across the social sciences to advance a critical and notably interdisciplinary theorisation of informal care Footnote 1 that troubles the narrow assumptions that continue to structure many mainstream renderings. More specifically, we aim to present a high-level overview of five key literatures which—notwithstanding their differing emphases—each contest the reification of informal care as an inherent good, and help to furnish a conception of informal care that both recognises informal care’s social value and underlines its embeddedness in systems and structures of inequality.

Informal (political) economies of care

Recent scholarship on the political economy of care—particularly that informed by critical feminist and Marxist perspectives—sheds light on the ways that informal care has been generated, co-opted, reconfigured, and even absorbed into the mechanisations of capitalism (e.g. Aulenbacher et al. 2018a , b ). A key tenet of this scholarship is that informal care entails a distinct paradox. Informal care is a critical pillar for the advancement of our increasingly financialised worlds, yet it is one that operates a shadow economy without representation in GDP or in other mechanisms societies use to account for value. Despite being the infrastructure that drives willingness and capacity for paid labour, informal care is (and has historically been) devalued and unrecognised (Henry 1987 ; Hallgrímsdóttir et al. 2011 ). This is most clearly evident in failures to financially compensate the caring activities that are carried out ‘informally’ (often by women) in the home, but pervades the broader scene of formal and informal care. Caring activities, scholars in this area attest, deliver considerable value to those being cared for as well as to societies and economy, yet they are rarely recognised as ‘real work’ deserving of payment (Glendinning and Kemp 2006 ; Fraser 2022 ). Informal economies of care thus involve the provision and/or exchange of care without financial return and, very often, at financial loss (to certain individuals). Insofar as financial benefits are generated, these are delivered to other actors within the scene, rather than to carers themselves. This perspective allows consideration of dialectical tensions between formal economy and the shadow economies of informal care. Yet, insofar as it recognises and advances remuneration for unpaid care work, it also invites a problematisation of the ‘formal’/‘informal’ care binary (see Fine 2007 , and Glucksmann 2005 , for a critique of this dichotomy).

For prominent scholars in this area, the tussle between the formal economy and the maintenance of informal care (Hartmann 1998 ) is critical to the successful operation of the financialised worlds in which they exist. So much so, in fact, that informal care economies can be seen as fundamental to the structures of financial capitalism. Feminist philosopher Nancy Fraser’s ( 2022 ) Cannibal Capitalism , for example, argues that capitalism is not just an economic system but an ‘institutionalised societal order’ (Fraser 2022 , p. 24). Within this order, she posits, a clear distinction is maintained between ‘the economy’ and a range of purportedly non-economic social spheres. Yet, it is these very social spheres that underpin and enable economic production. Capitalisms expansion depends on the ‘cannibalisation’ of these (concealed) sources of wealth.

From at least the industrial era onward […] capitalist societies have separated the work of social reproduction from that of economic production. Associating the first with women, and the second with men, they have enveloped reproductive activities in a cloud of sentiment, as if this work should be its own reward—or failing that, as if it need only be paid a pittance, unlike work done directly for capital, which is (in theory) paid a wage on which the worker can actually live. In this way, capitalist societies created an institutional basis for new, modern forms of women’s subordination. Splitting off reproductive labor from the larger universe of human activities, in which women’s work previously held a recognized place, they relegated it to a newly institutionalized domestic sphere where its social importance was obscured, shrouded in the mists of newly invented notions of femininity. And in this new world, where money became a primary medium of power, its being unpaid or underpaid sealed the matter: those who perform essential reproductive work are made structurally subordinate to those who earn living wages for surplus-value generating labor in the official economy, even as the work of the first is what enables the work of the second. (Fraser 2022 , p. 46)

The capitalist economy, Fraser notes, ‘free rides’ on “activities of provisioning, caregiving, and interaction that produce and maintain social bonds” (Fraser 2022 , p. 45), yet affords them no monetary value.

In this context, the devaluing of informal economies of care can be interpreted as a means of securing hegemonic power structures—often along race and gender lines. That is, the unequal division of care responsibilities functions to exclude some from the intergenerational accumulation of capital and assets, while concentrating power, resources, and wellbeing with others (e.g. Clement 1996 ; Gilligan 1982 ; Graham 1991 ; Poole and Isaacs 1997 ; Ungerson 1983 ). Women typically undertake significantly more informal care work than men, with ramifications for their ability to hold (well paid) jobs and be financially independent (e.g. Fine 2012 ; Mozhaeva 2021 ). The dominant social perception of women as inherently caring at a bio-social level can be understood, in this context, as a normative construction that offers a convenient rationale for allocating a disproportionate load of care responsibilities to women (Williams 2018 ; Jenkins 2020 ; Chambers 2001 ; Palmer and Eveline 2012 ) and subsequently reducing their capacity for asset accumulation or income generation. Similarly, the phenomenon whereby wealthier individuals (including women) outsource care responsibilities to others (often people of colour (e.g. Coe 2019 )) reproduces socio-economic inequalities in ways that are far from accidental. Informal care therefore emerges as a fraught paradox. On one hand, it is a locus of intuitive cultural value. On the other hand, it is a vehicle of inequality and cultural domination that reproduces patriarchal, class and racial oppression in the service of financialised capitalism.

What these political economic and feminist perspectives reveal is that the everyday undulations of informal care are shaped by histories, social structures, and interpersonal relations which afford differential obligations to be in caring relations (Folbre 2006 , 1994 ; Bittman et al. 2003 ; see also Fine 2007 , on the social division of care). Indeed, a key takeaway from this scholarship is that social, political, and economic structures heavily mediate the content of caring moments—creating needs and normative obligations and inflecting the emotional and material experience of informal care. In addition to a political economy of informal care, we might consequently talk about a moral economy of informal care (cf. Sayer 2000 , 2007 ) and the connections between forms of normativity and dominant (or naturalised) socio-material practices (see also Tronto 2020 ). This includes how participation in informal care can be deployed (and requested) as a moral commitment and/or coercive cultural form (cf. Sayer 2007 ). As well as how such moralities create regimes that disallow some emotional expressions (e.g. not wanting to care, or feeling angry, ambivalent or estranged) and compel care (by some) through moralities of shame and blame. As thus understood, the work of informal care does not occur naturally or inevitably in the context of (gendered and racialised) carer’s ‘innate’ caring dispositions, but rather emerges from and functions to reproduce the social, political, and economic hierarchies of our current world. Such emergences include affective pulls and tensions between entanglement and estrangement (Broom et al. 2020 ).

Affective entanglements of care

Another key literature for comprehending informal care concerns care’s affective dimensions (Ahmed 2004a , b ; Lehmann et al. 2019 ; Read 2022 ; Clough 2008 ; Blackman and Venn 2010 ; Mazzarella 2020 ). While Marxist-feminist analysis has denaturalised the gender and class structuring of patriarchal capitalism, it has also tended to focus on the expropriation of the physical labour of care, which has placed less emphasis on care’s “emotional, agentic and relational aspects” (Williams 2018 , p. 549). The ‘affective turn’ in humanities and social science scholarship, by contrast, has usefully drawn attention to the visceral, autonomous and (often) non-conscious vitalities and intensities that circulate within and between bodies and environments (Dragojlovic and Broom 2018 ; Stern 2019 ). This work underlines the embodied and relational nature of emotions, including those that constitute and animate caring relationships. Affect, as conceptualised here, arises “in the midst of in-between-ness : in the capacities to act and be acted upon […] in those intensities that pass body to body” (Seigworth and Gregg 2010 , p. 1). That is, emotions are seen as sematic, but also as fundamentally relational—creating what Sara Ahmed describes as “the very effect of the surfaces or boundaries of bodies and the worlds” (Ahmed 2004a ). For Ahmed ( 2004a , b ), individual subjects are single points in a broader circulation of emotions. This work underlines the emotional and affective complexity of being in (or outside of) care, arguing that caring relationality is assembled and reproduced through what Ahmed ( 2004a , b ) describes as ‘sticky’ associations. Associations, significantly, that bind some people together while separating others.

As intimated above, the affective turn has occurred in relation to concerns about embodiment, which frame care as visceral and sensory. Through this lens, care appears in/as ‘ordinary affects’ (Stewart 2007 ) such as a “tug at the heart strings” (Nussbaum 2001 , p. 325) or a sense of having been moved or touched. Care is an affective state, but it is also active and agentic. Through this lens, the significance of caring feelings lies, in part, in their propensity to animate movement and facilitate particular forms of knowing, acting, or relating (Stewart 2007 ). The capacity to care is intimately connected with the body’s capacity to affect and be affected, as well as with the body’s existence in a perpetual state of becoming (Blackman 2012 ; Seigworth and Gregg 2010 ; Dragojlovic and Broom 2018 ). To care is to feel and be moved by something at a bodily level. Care or carelessness might be present in the sensations and emotions experienced when passing someone or something, and in the embodied desire to stop or speed up. It might present as a feeling of needing to assist, or a compulsion to disappear when confronted with the other’s suffering. Such visceral pushes and pulls occur across the spectrum of relationships, with their intensities varying.

Lynch and colleagues (Lynch 2022 ; Lynch et al. 2009 , 2021 ; Cantillon and Lynch 2017 ) write at length about these affective dimensions of care, emphasising affective inequalities and injustices therein. Echoing key insights from the aforementioned scholarship on capitalism’s parasitic dependency on unpaid care work, Lynch and colleagues argue that inequalities are not only generated via the economic, cultural, and political systems of capitalism, but also via capitalism’s ‘affective system’. The problem of affective inequality, they posit, extends beyond disparities in who is expected to perform the unpaid yet socially and economically indispensable work of providing informal caring—or even who is expected to do the work of generating and performing caring feelings (see Hochschild 2012 ). Giving and receiving care is also a basic human need, and one that remains unmet in many people’s lives.

Human beings typically have both a need and a capacity for intimacy, attachment and caring relationships. The ability to recognise and feel some sense of affiliation and concern for others is a typical human trait, and everyone needs, at least sometimes, to be cared for. People generally value the various forms of social engagement that emanate from such relations and define themselves in terms of them. Solidary bonds of friendship or kinship are frequently what brings meaning, warmth and joy to life. Being cared for is also a fundamental prerequisite for human development. […] Being deprived of the capacity to develop supportive affective relations of love, care and solidarity, or of the experience of engaging in them when one has the capacity, is therefore a serious human deprivation for most people: it is a core dimension of affective inequality. (Lynch et al. 2009 , p. 1)

This understanding of care focusses attention not only to who is made to shoulder a disproportionate ‘burden’ of (physical and/or emotional) care work, but also who is or is not permitted and resourced to act on the human impulse to care. Equally, it asks us to consider “who needs (different degrees of) love, care and solidarity, which is all of humanity at different times” (Lynch et al. 2021 , p. 57) and whose care needs are or are not satisfied.

An important aspect of this equation, which is particularly relevant for understanding inequalities in informal care specifically, concerns inequalities in opportunities to give or receive love. Scholars thus talk about ‘love labour’—“the emotional work involved in loving a given person [which] cannot be assigned to another by a commercial or even a voluntary arrangement without undermining the premise of mutuality that is at the heart of intimacy” (Cantillon and Lynch 2017 , p. 170). As Sara Cantillon and Kathleen Lynch ( 2017 ) explain, research in the neurobiology of attachment attests to the important of emotionally intimate and non-substitutable connections in securing a sense of self and affording ontological stability. To be deprived of love, they conclude, is “a major social injustice” (Cantillon and Lynch 2017 , p. 170). Yet, emotions and affects (like other social resources) are not distributed evenly in society, and sticky associations mean that different bodies routinely experience differential opportunities to be in loving relationship. As Hillary McBride ( 2021 ) observes, ‘body hierarchies’—including the social devaluation of bodies that diverge from the white, thin, able-bodied ideal, as well as broader social and economic inequalities that map onto bodily differences—mediate access to care, include at the level of primary (loving) relations. Equally, occupying a place of social or economic privilege—for example, by virtue of being male, white, citizen, able-bodied or so forth—may afford some individuals more opportunities to do the affective/meaning work of care (e.g. expressing grief, intimacy, or love) while others are left to do the practical work of caring (e.g. meeting physical needs for hygiene, food, or shelter). This scholarship expands understandings of inequalities in informal care to include not only the need for freedom from the uneven burdens or responsibilities of care, but also for freedom to give and receive care as a basic human need (see Dragojlovic and Broom 2018 ; Gordon et al. 1996 ).

This focus on relationality and entanglements—including the ways that people are mutually constituted through their interactions—speaks to the value of care for individual and societal wellbeing. It also enables more nuanced analysis of the complexities of care as a lived and felt experience. Affect scholars note that care can take ambivalent and contradictory forms (Wetherell 2012 ), and that many injustices have been committed “in its name” (Lynch et al. 2021 , p. 57). Simple binaries between care ‘givers’ and care ‘recipients’ have been critiqued for both ignoring interdependencies and reproducing the unequal power dynamics that often inflect care-in-relation. Within and beyond primary relationships, this literature attests, ‘caring’ intentions can easily disintegrate into a fractious moral economy, with care becoming a discursive rationale for counterproductive forms of ‘help’ or intervention (e.g. Lavis et al. 2016 ; Klein 2020 ; Darling 2011 ; Hoggett 2006 ; Peterie 2023 ; Peterie et al. 2022 ). Indeed, care can be coercive, paternalistic, and even cruel (Berlant 2004 ; Sirriyeh 2018 ; Lavis et al. 2016 ). As a practice of binding and/or dividing, care occurs in the context of broader affective worlds, and at times reproduces hegemonic patterns of belonging and/or exclusion. Being able to ‘give’ care may therefore be a privilege while being in receipt of care may be a burden—a reality that challenges much of the empirical scene of informal care analysis and its focus on the ‘costs’ of caring (Molyneaux et al. 2010 ). This is usefully articulated as an affective tussle of care, with the scene of care creating trouble and tension rather than occupying a neutral moral territory. What this scholarship ultimately advances is a deeper recognition of the ‘messiness’ of care as an embodied entanglement—a scene of normative, affective, discursive relations, in which both the imposition of care and the desire to care are tied to the affective temporalities of the present (Ungerson 1987 ; Kittay 1999 ; Parker 1993 ).

The administration of care and neglect

As we have begun to see, informal care—typically carried out in the ‘private’ of everyday life—exists at the interstices of affect, institutional forms, political landscapes, and economic paradigms. Adding to this picture of intermingling relations, social policy scholars stress that access to tangible and intangible resources—from sick days, to parental and bereavement leave, to welfare safety nets—shapes caring relations and associated inequalities. This critical policy studies literature tends to explore the relationship between what is subsidised by the modern state and how it impacts the interpersonal ‘private’ lives of citizens and residents.

This area of scholarship has very often centred on the contemporary context of neoliberalism (Moss et al. 2006 ; see also Fine and Glendinning 2005 ). And, moreover, on the administration of care in the context of a gradual retraction in government supports across the political spectrum (Sevenhuijsen 2000 ) as neoliberalism bites into historical safety nets. As a political-economic project, neoliberalism is premised on a commitment to market principles as the rightful arbiter of human life (Grady and Harvie 2011 ). Personal liberty and liberation from government intervention—operationalised through the winding back of the welfare state and the weakening of regulatory frameworks to allow free trade and private entrepreneurship—has been a rallying cry of neoliberal reformers. Within the neoliberal schema, the individual’s core responsibility is “to find means of self-sustenance and not to be ‘assisted’ by society” (Amable 2011 , pp. 22–23). Insofar as social supports are available, neoliberal advocates insist that they should not be redistributive, but should rather be governed by the (moral) ideals of hard work and personal responsibility, such that even the provision of aid is embedded in relationships of ‘mutual obligation’ and exchange (Amable 2011 ). As Hasenfeld and Garrow ( 2012 , p. 301) summarise, neoliberalism “recasts the role of the welfare state by shifting responsibility from state to market and from the collective to the individual”.

From a critical social policy perspective, one of the greatest challenges to informal care in the late 20 th and now twenty-first century has been the (attempted) leveraging of community goodwill in efforts to shrink welfare states and (re)assemble care as a private responsibility as part of this neoliberal project (Pickard 2012 ; Berlant 2004 ). The contradiction of concurrent retraction (of state) and demands for expansion (of community) have occurred in various countries (e.g. Berlant 2004 ; Patenaude 2019 ), but David Cameron’s ‘Big Society’ in the UK is frequently evoked as a particularly striking example (Dowling and Harvie 2014 ). The Big Society Cameron envisaged involved the outsourcing of state support to the ‘shadow state’ (Wolch 1990 ) of the third sector and, significantly, an emphasis on market-based solutions to social needs (Espiet-Kilty 2016 ). An often remarked-upon aspect of projects of this kind is their rationalisation of austerity and welfare privatisation through a discursive valorisation of the turn to community-based and volunteer-led care, as well as societal/community entrepreneurship.

The Global Financial Crisis has brought these dynamics even further into focus, triggering new waves of austerity that double-down on dominant neoliberal logics of individualisation and responsibilisation. As Emma Dowling ( 2021 , p. 7) observes in the UK context ,

In the wake of the Global Financial Crisis, Britain has undergone a deep restructuring, most visible in the extensive austerity measures supposedly geared to enabling economic recovery. The austerity measures implemented post-crisis offloaded more of the cost of care from the state onto individuals, households and communities. There is an emotional dimension to austerity, too. The implementation of such measures affects how people think of themselves and of others and how they seek to act in the world. Austerity measures serve to convince individuals that the only person they can truly rely on is themselves, supported, at best, by their family, and implying a greater reliance on informal support and charity provision. Yet the crisis obscures as much as it allows us to see: austerity measures that offload the cost of care onto the shoulders of the most disadvantaged in society are fuelled less by necessity than by an ideological agenda.

As Dowling’s ( 2021 ) analysis makes clear, the turn to community and the family to meet social needs has gone hand in hand with the systematic weakening of supports for caring families, communities, and societies (Hoppania and Vaittinen 2015 ). Informal caring relations have been ‘lent on’ even more than previously to perform functions hitherto performed by the state, even as they have been derailed through concerted political efforts to remove the state supports that underwrite capacity and willingness to provide (intensive) informal care to begin with (Pickard 2012 ). As Liz Lloyd ( 2023 , p. 1) observes, “unpaid care is in a particularly perilous situation because with social care policies confined to the margins, unable to attract political attention or economic resource, pressures on unpaid carers with continue unabated”.

The winding back of state support for caring activities specifically (e.g. via parenting and carers payments) has been a frequently emphasised part of this picture, but so too has the broader dismantling of many aspects of the traditional welfare state, including by attaching stringent conditions to the income support payments offered, for example, to people experiencing unemployment and acute financial stress (e.g. Watts and Fitzpatrick 2018 ; Peterie et al. 2019 ). Such developments, it has been noted, have coincided with changes in the labour force—such as a shift towards ‘gig’ and ‘disruptive’ economies (Vallas and Schor 2020 )—leaving many unemployed and employed workers with few options during key life moments that require high levels of care (death and dying, childbirth, mental health problems etc.). As Watts and Fitzpatrick ( 2018 , p. 100) note in their book Welfare Conditionality, the utilisation of financial ‘sanctions’ against welfare recipients who fail to meet the behavioural conditions associated with their payments has further been implicated in “food insecurity, serious housing problems, disconnection from electricity and phone connections, and inability to secure necessary medical care”, as well as a range of adverse social and emotional impacts. This has served to increase the hardship of individuals and families who are already experiencing financial stress, placing significant pressure on informal care relations.

What this scholarship makes clear is that ‘light touch’ and disciplinary approaches to everyday welfare have done considerable damage to already fragile informal care relations (Pfau-Effinger 2005 ). As Joan Tronto ( 2017 , pp. 34–35) puts it,

The inadequacy of the neoliberal model of the self-mastered self can only be elided by the move that President Bush made: to locate individuals into a family where they are willing to make sacrifices. However, there is no magic intrinsic to families to provide adequate care; they require material, social and psychic resources to thrive. […] [A]s more care responsibilities are being thrown back on families, their capacity to cope with problems increasingly rests upon their prior market success. This is no way to provide adequate care in society. The interesting new neoliberal response – to call for ‘resiliency’, another personalised trait for coping with misfortune – is now widely investigated in the social sciences as a cure for these neoliberal-induced ills.

Present-day inflationary and cost-of-living pressures will only raise the stakes at the intersections between contracting economies, basic incomes, structural austerity, and willingness to care (Birnbaum 2012 ). The most vulnerable to these impacts, scholars warn, will be those with the least means, rights and/or assets—a fact that will undoubtedly create heightened care needs within already disadvantaged communities. The individualisation of responsibility through the weakening of welfare safety nets thus creates and perpetuates the conditions of harm that make (crisis-focussed) informal care necessary to begin with. Understood in this way, informal care and associated inequalities represent a problem for and with contemporary social policy and administration.

Spaces and places of care

Recent research in human geography makes several contributions that further illuminate different but critical dimensions to informal caring relations and associated inequalities. Among other important interventions, this work draws attention to the central role of space and place in the practice and politics of informal care (e.g. Bowlby 2012 ; Cox 2013 ; Lawson 2007 ; Milligan and Wiles 2010 ); it shows that the embodied and affective experience of care is fundamentally shaped by the physical spaces, institutional contexts, political landscapes, and relational and community settings in which care occurs (Power and Hall 2018 ). Forms of material organisation, city planning, and suburban and rural configurations, for example, enable, disable, or otherwise inflect care at the micro-level. Such spatialities of/to care are in turn shaped by and reflective of the broader social, political, and economic landscape. The turn towards place and space as fundamentally mediating care provides a critical addition to the tools outlined thus far focussing on political economy, critical affect studies, and social policy and administration.

Just as political economy and social policy scholars underline the role of financial resources (including income support payments and associated safety nets) in facilitating or undermining informal care (e.g. Fine 2012 ; Cantillon and Lynch 2017 ; Dowling 2021 ; Tronto 2017 ), so too do human geographers stress the way that physical spaces shape care relationships, including care inequalities. Scholars emphasising space and place have demonstrated, for example, that rapid urbanisation has changed the everyday realities of informal care by shaping civil society, norms of solidarity, access to green spaces, and formal and informal care infrastructures (Bowlby 2012 ). Where elderly people may have previously relied on nearby family members for support, the dispersal of families across states and continents means that increasing numbers of older people now lack nearby networks of informal care (Milligan and Liu 2015 ). Viewed through this lens, the inequalities of proximity thus come to the fore, with, for instance, many young parents now lacking physical access to parents and relatives who might otherwise have assisted with child raising (Bose 2013 ). Such ‘carescapes’ (Bowlby 2012 ) alter the context of care, pushing (some) people into formal care institutions such as nursing homes and childcare facilities, while placing care outside the reach of others. Equally, the rise of apartment living and the transience of apartment dwellers in countries like Australia (in part due to the geographic patterning of employment and affordable housing) is reshaping the experience of informal care within and beyond the home, particularly vis-à-vis parenting (see Kerr et al. 2018 , 2021 ). As Sophie Bowlby ( 2012 , p. 2106) summarises, “relationships are relations in space and place”; even minor spatial changes such as “the distances separating the dwellings of generations within a family or of the living environments of older and younger people” can subsequently have significant impacts on how relationships are practised. For some care scholars, the provision of appropriate (non-commodified) ‘space to care’ (Care Collective 2020 ) is therefore an essential element in the curation of more caring communities.

The impacts of spatiality on care are perhaps most evident in the geographical patterning of access to local social and healthcare services, secure employment, safe and affordable housing, and green spaces, among other infrastructures and resources (McEachan et al. 2018 ). In the context of increasingly marketised and privatised health and social care systems in many countries, services are often concentrated in wealthy urban areas where profit margins are greatest (Kessler 2003 ). Similarly, residents in some urban locations—particularly economically disadvantaged suburbs and areas with high levels of social housing—experience reduced access to health services (e.g. Malmgren et al. 1996 ; Rosenberg 2014 ). These economic and infrastructural shortfalls intersect with other inequalities to produce marked disparities in health outcomes and create additional care needs in some postcodes, all the while transferring the burden of care in these locales from state-funded services to informal carers. Loïc Wacquant’s ( 2008 ) concept of territorial stigmatisation adds further detail to this picture of geographically patterned inequality in access to care. Wacquant shows that the ‘taint’ of place is a defining feature of contemporary marginalisation. Indeed, he posits that stigma becomes attached to place, and is “arguably the single most protrusive feature of the lived experience of those trapped in these sulphurous zones” (2008, p. 169). Some institutions—from prisons to mental health hospitals—have similarly been identified as loci of stigma where residents are marked as other. This taint stays with the individual after their release from the institution, as they are denied access to societal markers of decency and self-sufficiency (including, for example, access to a stable home) and instead relegated to other stigmatised spaces such as homeless shelters and half-way houses (Keene et al. 2018 ). What we see here is that institutional spaces of care and/or coercion embody and reproduce social, cultural, political, and economic hierarchies. Stigma—including the sticky stigmatisation of (some) geographic places—functions as an instrument of control (Tyler and Slater 2018 ), naturalising (class and race) inequalities and patterning both the need for and the experience of informal (and, indeed, formal) care.

Ecologies of care and kinship

To date, the informal caring literature—much like the care literature more broadly—has been dominated by work on human–human relations. Indeed, even the broad definition of informal care with which we began this article was adapted from a longer definition that conceptualised care as “a species activity” (Fisher and Tronto 1990 , p. 40); that is, as “one of the features that makes people human” (Tronto 1998 , p. 16). As the care crisis intersects with larger ecological and environmental crises, a growing number of scholars are calling for care scholarship (as well as care practice) to move beyond the bounds of the human to centre ecological and multispecies relations, including inequalities and injustices therein (see, for example, Lynch 2022 , on veganism). Such work is often deeply informed by First Nations literatures and practices (e.g. Quinn et al. 2022 ). Decentring the human, these scholars insist, promises to provide a more panoramic view of the extensive (often unpaid) care work that occurs across societies and species; of the multi-directionality of care; and of the embeddedness of even human–human caring relations in broader ecologies and intergenerational knowledges. These interdependencies have been routinely emphasised within First Nations scholarship on care, as well as within scholarship from the Global South, but, until recently, have received significantly less emphasis in the broader care literature.

The healthy land-healthy people in First Nations-centred scholarship (Burgess et al. 2009 ) speaks to the critical principle of relationality beyond the human in care, and to the value of collectivities that are rarely fostered in modern societies. The dominating (colonial) concepts of ownership, extraction, and exploitation (of non-human animals, or land and waterways) have decentred the historic and enduring ways that multidimensional care between humans, animals, and ecologies is practiced within First Nations communities. Care, as an ecological concept, grounded in First Nations knowledges, becomes longer (stretching over time) and wider (extending to species, land and waterways). As Whitt et al. ( 2001 , p. 4) explain,

Indigenous responsibilities to and for the natural world are based on an understanding of the relatedness, or affiliation, of the human and non-human worlds, which is best understood in its primary – genealogical – context. Genealogies provide stories of origins. They tell a person, or a people, where and from whom they are descended. In this sense they bind through time, showing how ancestors and descendants course together through a continuous, unfolding history.

Such understandings of care and relatedness ask us to confront the consumption and waste of human cohabitation with other species, and to see our footprint on country. Moreover, thinking about informal caring relations in ancestral, ecological, and multispecies terms means recognising the porousness of the boundaries that divide person, ancestor, descendant, animal, country, ecology, and so on (Barla and Hubatschke 2017 ). From this viewpoint, care between living human persons can never be simply between these persons. Human relations of care exist within a broader genealogical and ecological context and are necessarily intertwined with the provision or withholding of care to/from others in this broader whole.

In her book Staying with the Trouble, feminist STS scholar Donna Haraway ( 2016 ) makes an analogous point about the interdependency of all life using the metaphor of the compost pile.

Critters are at stake in each other in every mixing and turning of the terran compost pile. We are compost, not posthuman; we inhabit the humusities, not the humanities. Philosophically and materially, I am a compostist, not a posthumanist. Critters – human and not – become-with each other, compose and decompose each other, in every scale and register of time and stuff in sympoietic tangling, in ecological evolutionary developmental earthly worlding and unworlding. (p. 97)

As Haraway ( 2016 , p. 100) sees it, no species acts alone—“not even our own arrogant one pretending to be good individuals in so-called modern Western scripts”. She thus argues for a radical recomposition of kin: a recognition of all species as ‘family’, and a parallel extension of care beyond the bounds of the human. Such a reframing promises to radically transform and expand how we think about informal care particularly, as informal care frequently occurs in the context of relational commitments to kith and kin.

For many scholars in the environmental humanities, the ethic of care that animates such calls represents a powerful counterpoint and challenge to the individualistic logics of late capitalism. As Stefanie Barca ( 2020 ) observes, economic reproduction depends not only the expropriation of (formal and informal) care work from women, colonised and racialised subjects, but also on the expropriation of the (unremunerated) reproduction undertaken by nature (see also Fraser 2022 ).

[I]f the nexus between women and non-human nature as co-producers of labour power has been socially constructed through capitalist relations of reproduction, then women’s environmental and reproductive struggles are to be seen as part of the general class struggle. For socialist ecofeminists, this requires disavowing the paradigm of modern economic growth, because the latter has subordinated both reproduction and ecology to production, considering them as means to capitalist accumulation. (Barca 2020 , p. 34)

Scholars in this tradition stress the importance of resisting “the master version of modernity by countering the subordination of life to social imperatives of production/accumulation” (Barca 2020 , p. 39). Central to this project is “seeing and valuing the forces of reproduction” or—to both echo and expand Fisher and Tronto’s ( 1990 ) species-centric definition of care—to make visible the constitutive role the more-than-human plays in ‘maintaining’ and ‘repairing’ our world. A more expansive conceptualisation of (informal) care ensues.

The story of care, as we weave across these fields and foci, becomes increasingly unwieldy. Care is revealed as multivalent in character—reaching across scales and generations, involving evolving (urban) materialities, and having a distinctly more-than-human feel. When we examine informal care in particular, what emerges is a set of enduring and developing concerns, articulated through often disparate but collectively critical scholarships centred on political economy, affect, policy, space, and ecologies. Despite their often-separate ‘treatment’, these spheres of concern swirl around each other in everyday life, choreographing interpersonal and human–ecological relations. They mediate life in all its forms, rippling unevenly across (certain) bodies, times, communities, and spaces in ways that frequently (re)produce inequality.

Together, the literatures discussed in this article furnish a nuanced conceptualisation of informal care that underlines its uneven and often extractive contours. These literatures reveal informal care as a critical—but frequently exploited and undervalued—pillar of our financialised worlds. As an affective relation and resource that is not distributed evenly in societies. As a ‘private’ (and, increasingly, privatised) activity that is fundamentally dependent on (evaporating) public infrastructures. As a spatial practice that is necessarily shaped by our physical environments and inequalities therein. And a more-than-human relation that spans species, bodies, and generations in ways that make ecological degradation part of this broader scene of troubled care.

Informal care, then, is best viewed as an entangled, temporal, material, multispecies emergence, which requires a sophisticated array of concepts to capture its everyday complexity and avoid reductionism (i.e. to the mere social, to the non-material, to the only-human, to the present, and so on). The conceptual tools we have presented in this article are highly valuable for the window they offer into this complexity and issues of inequality therein. They are also vital for capturing the multidimensional nature of the various and evolving conditions that work against the desire and willingness to care, undermining the vitality of our interconnectedness and, by extension, the future of our social and planetary worlds.

We note that while many of these theories pertain to or could be applied in the context of diverse spheres of care, our focus, in this article, is on informal care specifically—particularly informal care as carried out in the Global North in the context of advanced capitalism. For critical discussions of formal and informal care across scales and countries, see, for example, Williams ( 2018 ) and Aulenbacher et al. ( 2018a , b ).

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Peterie, M., Broom, A. Conceptualising care: critical perspectives on informal care and inequality. Soc Theory Health 22 , 53–70 (2024). https://doi.org/10.1057/s41285-023-00200-3

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One Piece's Final Villain Has a Surprising Inspiration & It Explains Everything

One piece episode #1104 release date & time, vegapunk's real-life inspiration in one piece reveals his true motives.

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Vegapunk Hints At Imu's Gender and True Identity

One piece created by eiichiro oda, chapter #1113, " stalemate ".

Thus far, Imu has appeared in silhouette only and chapter #1085 is the only instance Imu is seen speaking. In the chapter, Imu referred to themself in the third person using "mu" and, as the anime has yet to adapt the chapter, it was impossible to be sure of Imu's gender. That said, Emporio Ivankov proposed an interesting theory in chapter #1086 when he revealed that there was a monarch among the first twenty rulers named St. Imu who belonged to the Nerona Dynasty.

While the "saint" prefix is commonly used for both male and female World Nobles in the English translation, in Japanese, there are two separate terms for each. Male Celestial Dragons are called " sei " while female Celestial Dragons are called " gū". As such, Saint Imu is referred to as "Imu-sei" in the original Japanese text , confirming that the original Imu was indeed male.

Judging by how Imu seemed to know Nefertari Lily well and was aware of her blunder as well as the true history of the D Clan and the poneglyphs, it is safe to assume that the Imu seen in the present is the same Imu Nerona from 800 years ago. As Ivankov proposed, someone had to have proved the Op-Op Fruit's ability to grant eternal life, and this may very well be Imu and the Five Elders. All these facts add up to prove that the mysterious man Vegapunk mentions in chapter #1113 is very likely Imu.

Vegapunk's Message Casts Doubt on Imu's Morality

The world government and the ancient kingdom may not be wholly good or evil.

As a man of science with rather complex morals himself, Vegapunk is certainly right to assume that he is in no place to make moral judgments. Though Imu has largely been considered an evil tyrannical ruler, Vegapunk's words suggest there may be more to his character and story than meets the eye. A few fan theories have even entertained the idea that the Ancient Kingdom may not have been as good and innocent as fans have been led to believe and that perhaps the World Government's strict authoritarian rule was born out of necessity.

Given One Piece 's nuanced approach to morality, such a twist would certainly be possible. According to Professor Clover, it is the ideals of the Ancient Kingdom that the World Government deemed dangerous and tried to bury, which may be related to freedom given the implied connection between Sun God Nika and the Ancient Kingdom.

Nevertheless, while freedom is presented as a positive ideal through the lens of the pirates, it is entirely possible that unrestricted freedom nearly brought the world to the edge of destruction in the past. This may be especially true if the Ancient Kingdom, which Vegapunk revealed was more scientifically advanced than Egghead, did in fact create the Ancient Weapons as popularly speculated. The dangers of unrestricted scientific advancement are well-known in real life and have been discussed even in One Piece .

It is possible that Imu deems his actions necessary for the greater good of the world and even the World Government's plans for a great cleansing simply serve to restore the equilibrium they believe is necessary. The Great Pirate Era has caused plenty of upheaval and while there are technically "good" or rather "morally just" pirates like the Straw Hats, there are also those like Kaido who have caused nothing but suffering to innocent people while aspiring towards freedom.

What Imu's Immortality Means for One Piece

At this point, it is all but inevitable that Imu will be one of the series' primary antagonists in the Final Saga alongside Blackbeard. Imu being immortal not only makes him appear more intimidating but also adds a poetic sense of meaning to his inevitable confrontation with Luffy, who has inherited the will of Imu's oldest enemies, the D Clan.

Imu's immortality does not necessarily equate to him being invincible , but he will surely not be an easy opponent by any means. Imu's gender does not invalidate the theories about his connection to the sea, and given the brief glimpse of his powers in chapter #1085, he may be just as troublesome to deal with as the Five Elders .

Overall, as seen from Neferatri Cobra's death, Imu's existence is a secret learned at the cost of death and very few have seen him and lived to tell the tale. It is still unclear how Vegapunk managed to learn of Imu's existence without the Five Elders' knowledge, but it is possible that Ohara's findings and Shaka's research may have uncovered the truth. This raises the interesting question about just how many secrets Vegapunk knows about the world of One Piece which could be revealed in his message.

One Piece is available from Manga Plus and Viz Media.

Created by Eiichiro Oda, One Piece is a multimedia franchise that began as a manga series and follows the adventures of the Straw Hat Pirates as led by Monkey D. Luffy. Luffy, an enthusiastic pirate with a thirst for adventure, is afflicted by a mysterious curse that gives him various powers he uses to protect himself and his friends. The manga eventually gave way to the anime series, with the two being some of history's longest-running anime and manga series. Along with over fifty video games made over the years, the series entered the live-action world with Netflix's 2023 adaptation.