action hypothesis formation

Emergent Learning

Action Hypotheses

What are action hypotheses.

Action Hypotheses are a form of “if/then” hypothesis that we use in Emergent Learning. While scientific hypotheses propose an explanation of phenomena based on evidence from the past, action hypotheses look ahead to explain what we expect to happen as a result of future action. In both cases, the goal is to articulate something that is testable.

Action Hypotheses explore the thinking about a result we hope to accomplish (the “then”) and what we think it will take to get there (the “if”). This kind of hypothesis is so fundamental to our thinking every day that we couldn’t take action without it. It’s the smallest kernel of how we think. For example:

When and how do we use Hypotheses in Emergent Learning?

When people in an Emergent Learning culture say, “my hypothesis is…”, it is not to demonstrate authority by citing scientific language. Rather, they are communicating in shorthand that “I am making my thinking transparent to you and inviting you to inquire about it or offer an alternative.” It is intended to grow our collective thinking and agency.

EL practitioners learn to listen for hypotheses in conversations and ask questions to make sure we understand the thinking of our colleagues in a team or initiative. We use them to tease out the thinking implicit in Theories of Change. We read for them in narratives and use them to write stronger narratives. We use them to create more powerful connections between learning and evaluation by expressing hypotheses and planning how to test them out in our work over time, so that we track not just results but learn about and refine the thinking that got us there.

Hypotheses also play a starring role in Emergent Learning Tables. The whole aim is to collect all of our experience and data and make meaning of it in order to express our best possible thinking about what it will take to succeed going forward — expressed in the form of one or more Action Hypotheses.

Thinking of strategies or actions as “hypotheses” reminds us that what we thought was “true” or “right” might not be. Using this language invites us to remember that there is usually more than one path to consider and that maybe it’s OK to hold and test more than one hypothesis at a time — as long as we actually circle back and test them!

Formulation of action hypothesis

• 1 FORMULATION OF ACTION HYPOTHESIS
• 2.1 WHAT IS A HYPOTHESIS?
• 3.1 CHARACTERISTICS OF A GOOD ACTION HYPOTHESIS
• 3.2 DIFFERENT FORMS FOR STATING ACTION HYPOTHESIS
• 3.3 FORMULATION OF AN ACTION HYPOTHESIS
• 3.4 Illustration of an action hypothesis in four different forms

FORMULATION OF ACTION HYPOTHESIS

This section helps you in understanding how to formulate an action hypothesis.

WHAT IS A HYPOTHESIS?

You might be wondering what an action hypothesis is?

The processes, an investigator may use to examine a problem in the field of education are similar to the ones we use to attack our day to day problems.

Look at the following example.

A teacher notices that one of her Students in the IV grade does not show progress in learning “addition of two digit numbers”. Careful observation of this child in the classroom may suggest several possible causes for this problem. This in turn will help the teacher think of suitable remedies.

Based on these possible causes the teacher states HYPOTHESES which are the guessed strategies for solving the problem. Then the teacher designs and carries out a programme aimed at testing each hypothesis and checking the child’s progress.

Without ‘guessing’ the possible causes the teacher can not plan any remedy for the problem.

Once the investigator diagnoses the causes of the pinpointed/specific problems, he/she starts thinking about what concrete action, if taken, would bring about the desired change/solution.

Then he/she formulates hypothesis specifying the immediate ‘actions’ that could be taken to solve the problems.

The hypotheses formulated in action research are called ACTION HYPOTHESES

CHARACTERISTICS OF A GOOD ACTION HYPOTHESIS

A good action hypothesis should be

• Logically related to the problem
• Testable in classrooms situations
• Clearly stated without ambiguity
• Directly stated in terms of the expected outcome (should not be a generalized statement)
• Testable within a considerably short time (maximum of three months)

DIFFERENT FORMS FOR STATING ACTION HYPOTHESIS

a) Declarative form: An action hypothesis may be formulated as a statement with a positive relationship between the two factors identified, one being the cause and the other being the effect. This is also called a directional hypothesis.

b) Predictive form: An action hypothesis clearly predicting the expected out come which would emerge after the action plan is implemented. This can be stated using ‘if and then’ statement.

c) Question form: Questions can be raised as action hypotheses as what would be the result of the intended action plan.

d) Null form: A null hypothesis states that no relationship exists between the factors considered in the problems. This form is mostly used when rigorous statistical techniques are to be used.(A thoroughly worked out example for all these forms is given in the next unit.) Thus, an action hypothesis provides clarity and direction to solve a problem. Hence it is considered an important stage in action research.

FORMULATION OF AN ACTION HYPOTHESIS

To form a hypothesis the investigator should

• Have a thorough knowledge about the problem
• Be clear about the desired goal (solution)
• Make a real effort to look at the problem in new ways other than the regular practices (come out form conventional thinking)
• Give importance for imagination and speculation
• Think of many alternative solutions.
• Thoroughly examine the conditions/contexts in which the problem exists and then
• State the hypothesis

Illustration of an action hypothesis in four different forms

Here is an illustration of an Action Hypothesis stated in different forms. Carefully observe the wordings, the format, relationship between the factors in each form of the hypothesis. Predictive form Declarative orDirectional Form QuestionForm Null Form If the III grade students receive a “drill work” in the chapter “Addition of whole numbers their progress will be better in Arithmetic. 1. Replace the word “Drill Work” as ‘Supervised study’ in all the forms. 2. Add after, addition of two digit (carrying) A “Drill work” program in the chapter addition of whole numbers for III grade students will cause/influence better progress in Arithmetic, Or Addition (whole number) drill work in and progress in Arithmetic are (positively) related to each other.OrThere is a (positive) relationship between ‘Drill work’ in Addition (whole Nos.) and progress in Arithmetic. To what extent a “Drill work” program in the chapter Addition (Whole numbers) for III grade students will improve their progress in Arithmetic.OrDoes a drill work program in ‘Addition (Whole Nos.) for III graders improve their progress in Arithmetic? If so, to what extent? A “Drill work” program in the chapter. ‘Addition for III grade students and their progress in Arithmetic are not related to each other.OrThere is no significant relationship between the ‘drill work’ program in the chapter addition and progress (whole No.) in Arithmetic among III grade students.

Activity Sheet on Formulation of action hypothesis

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Research Hypothesis In Psychology: Types, & Examples

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

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

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

Some key points about hypotheses:

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

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

Types of Research Hypotheses

Alternative hypothesis.

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

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

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

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

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

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

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

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

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

Null Hypothesis

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

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

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

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

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

Nondirectional Hypothesis

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

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

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

Directional Hypothesis

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

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

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

Falsifiability

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

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

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

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

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

Can a Hypothesis be Proven?

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

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

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

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

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

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

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

How to Write a Hypothesis

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

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

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

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

More Examples

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

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Methodology

• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

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

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

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1. ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

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

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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

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

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

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The Logic of Action

In this article we provide a brief overview of the logic of action in philosophy, linguistics, computer science, and artificial intelligence. The logic of action is the formal study of action in which formal languages are the main tool of analysis.

The concept of action is of central interest to many disciplines: the social sciences including economics, the humanities including history and literature, psychology, linguistics, law, computer science, artificial intelligence, and probably others. In philosophy it has been studied since the beginning because of its importance for epistemology and, particularly, ethics; and since a few decades it is even studied for its own sake. But it is in the logic of action that action is studied in the most abstract way.

The logic of action began in philosophy. But it has also played a certain role in linguistics. And currently it is of great importance in computer science and artificial intelligence. For our purposes it is natural to separate the accounts of these developments.

1.1 Historical overview

1.2 the stit saga, 1.3 intentions, 1.4 logics of special kinds of action, 2.1 speech acts, 2.2 action sentences, 2.3 dynamic semantics, 3.1 reasoning about programs, 4.1 representing and reasoning about actions, 4.2 description and specification of intelligent agents, other internet resources, related entries, 1. the logic of action in philosophy.

Already St. Anselm studied the concept of action in a way that must be classified as logical; had he known symbolic logic, he would certainly have made use of it (Henry 1967; Walton 1976). In modern times the subject was introduced by, among others, Alan Ross Anderson, Frederick B. Fitch, Stig Kanger, and Georg Henrik von Wright; Kanger’s work was further developed by his students Ingmar Pörn and Lars Lindahl. The first clearly semantic account was given by Brian F. Chellas (1969). (For a more detailed account, see Segerberg 1992 or the mini-history in Belnap 2001.)

Today there are two rather different groups of theories that may be described as falling under the term logic of action. One, the result of the creation of Nuel Belnap and his many collaborators, may be called stit theory (a term that will be explained in the next paragraph). The other is dynamic logic . Both are connected with modal logic, but in different ways. Stit theory grew out of the philosophical tradition of modal logic. Dynamic logic, on the other hand, was invented by computer scientists in order to analyse computer action; only after the fact was it realized that it could be viewed as modal logic of a very general kind. One important difference between the two is that (for the most part) actions are not directly studied in stit theory: the ontology does not (usually) recognize a category of actions or events. But dynamic logic does. Among philosophers such ontological permissiveness has been unusual. Hector-Neri Castañeda, with his distinction between propositions and practitions, provides one notable exception.

The stit tradition is treated in this section, the dynamic logic one in the next.

The term “stit” is an acronym based on “sees to it that”. The idea is to add, to an ordinary classical propositional language, a new propositional operator \(\stit\), interpreting \(\stit_i\phi\), where \(i\) stands for an agent and \(\phi\) for a proposition, as \(i\) sees to it that \(\phi\). (The official notation of the Belnap school is more laborious: [\(i \mathop{\mathsf{stit:}} \phi\)].) Note that \(\phi\) is allowed to contain nestings of the new operator.

In order to develop formal meaning conditions for the stit operator \(\stit\) a semantics is defined. A stit frame has four components: a set \(T\), the nodes of which are called moments; an irreflexive tree ordering \(\lt\) of \(T\); a set of agents; and a choice function \(C\). A maximal branch through the tree is called a history.

The tree \((T,\lt)\) seems to correspond to a naïve picture familiar to us all: a moment \(m\) is a temporary present; the set \(\left\{n : n \lt m\right\}\) corresponds to the past of \(m\), which is unique; while the set \(\left\{n : m \lt n\right\}\) corresponds to the open future of \(m\), each particular maximal linear subset of which corresponds to a particular possible future.

To formalize the notion of action, begin with two general observations:

• usually an agent is not able to select one possible future to become the unique actual future, but
• by his action he can make sure that certain futures, which before his action are possible, are no longer possible after his action.

This is where the choice function \(C\) comes in: for each moment \(m\) and agent \(i, C\) yields a partitioning \(C_i^m\) of the set \(H_m\) of all histories through \(m\). An equivalence class in \(C_i^m\) is called a choice cell. (Note that two histories belonging to the same choice cell agree up to the moment in question but not necessarily later on.) If \(h\) is a history running through \(m\) we write \(C_i^m(h)\) for the choice cell of which \(h\) is a member. It is natural to associate \(C_i^m\) with the set of actions open to the agent \(i\) at \(m\), and to think of the choice cell \(C_i^m(h)\) as representing the action associated with \(h\).

A stit model has an additional component: a valuation. A valuation in a frame, it turns out, is a function that assigns to a variable and each index either 1 (truth) or 0 (falsity), where an index is an ordered pair consisting of a history and a moment on that history. The notion of truth or falsity of a formula with respect to an index can now be defined. If \(V\) is the valuation we have the following basic truth-condition for atomic \(\phi\):

The truth-conditions for the Boolean connectives are as expected; for example,

Let us write \(\llbracket\phi\rrbracket_m\) for the set \(\left\{h \in H_m : (h,m) \models \phi\right\}\), that is, the set of histories agreeing with \(h\) at least up to \(m\) and such that \(\phi\) is true with respect to the index consisting of that history and \(m\). Defining formal truth-conditions for the stit operator there are at least two possibilities to be considered:

• \((h,m) \models \stit_i \phi\) iff \(C^{m}_i(h) \subseteq\llbracket\phi\rrbracket_m\).
• \((h,m) \models \stit_i \phi\) iff \(C^{m}_i(h) \subseteq\llbracket\phi\rrbracket_m\) and \(\llbracket\phi\rrbracket_m \ne H_m\).

To distinguish the two different operators that those conditions define, the former operator is called the Chellas stit , written \(\cstit\), while the latter operator is called the deliberative stit , written \(\dstit\).

In words, \(\cstit_i \phi\) is true at an index \((h,m)\) if \(\phi\) is true with respect to \(h'\) and \(m\), for all histories \(h'\) in the same choice cell at \(m\) as \(h\); this is called the positive condition. The truth-condition for \(\dstit_i \phi\) is more exacting; not only the positive condition has to be satisfied but also what is called the negative condition: there must be some history through \(m\) such that \(\phi\) fails to be true with respect to that history and \(m\).

Both \(\cstit\) and \(\dstit\) are studied; it is claimed that they capture important aspects of the concept “sees to it that”. The two operators become interdefinable if one also introduces the concept “it is historically necessary that”. Using \(\Box\) for historical necessity, define

Then the formulas

are true with respect to all indices.

One advantage of stit theory is that the stit analysis of individual action can be extended in natural ways to cover group action.

A number of the initial papers defining the stit tradition are collected in the volume Belnap 2001. One important later work is John F. Horty’s book (2001). The logic of stit was axiomatized by Ming Xu (1998).

Michael Bratman’s philosophical analysis of the notion of intention has had a significant influence on the development of the logic of action within computer science. It will be discussed below.

In a series of papers Carlos Alchourrón, Peter Gärdenfors and David Makinson created what they called a logic of theory change, later known as the AGM paradigm. Two particular kinds of change inspired their work: change due to deontic actions (Alchourrón) and change due to doxastic actions (Gärdenfors and before him Isaac Levi). Examples of deontic actions are derogation and amendment (laws can be annulled or amended), while contraction and expansion are analogous doxastic actions (beliefs can be given up, new beliefs can be added). Later the modal logic of such actions has been explored under the names dynamic deontic logic , dynamic doxastic logic and dynamic epistemic logic . (For the classic paper on AGM, see AGM 1985. For an introduction to dynamic deontic logic and dynamic doxastic logic, see Lindström and Segerberg 2006. We will return to this topic in Section 4, where it is viewed from the perspective of the field of artificial intelligence.

2. The Logic of Action in Linguistics

In linguistics, there are two ways in which actions play a role: on the one hand, utterances are actions and on the other they can be used to talk about actions. The first leads to the study of speech acts, a branch of pragmatics, the second to the study of the semantics of action reports, hence is of a distinctly semantic nature. In addition to this, there is a special type of semantics, dynamic semantics, where meanings are not considered as state descriptions but as changes in the state of a hearer.

The study of speech acts goes back to Austin (1957) and Searle (1969). Both emphasise that using language is to perform certain acts. Moreover, there is not just one act but a whole gamut of them (Austin himself puts the number in the magnitude of \(10^3)\). The classification he himself gives involves acts that are nowadays not considered as part of a separate science: the mere act of uttering a word (the phatic act ) or sentence is part of phonetics (or phonology) and only of marginal concern here. By contrast, the illocutionary and perlocutionary acts have been the subject of intense study. An illocutionary act is the linguistic act performed by using that sentence; it is inherently communicative in nature. By contrast, the perlocutionary act is an act that needs surrounding social contexts to be successful. The act of naming a ship or christening a baby, for example, are perlocutionary. The sentence “I hereby pronounce you husband and wife” has the effect of marrying two people only under certain well-defined circumstances. By definition, perlocutionary acts take us outside the domain of language and communication.

Searle and Vanderveken (1985) develop a logic of speech acts which they call illocutionary logic . This was refined in Vanderveken 1990 and Vanderveken 1991. Already, Frege used in his Begriffsschrift the notation “\(\vdash \phi\)”, where \(\phi\) denotes a proposition and “\(\vdash\)” the judgment sign. So, “\(\vdash \phi\)” says that \(\phi\) is provable, but other interpretations of \(\vdash\) are possible (accompanied by different notation; for example, “\(\models \phi\)” says that \(\phi\) is true (in the model), “\(\dashv \phi\)” says that \(\phi\) is refutable, and so on). An elementary speech act is of the form \(F(\phi)\), where \(F\) denotes an illocutionary point and \(\phi\) a proposition. In turn, an illocutionary force is identified by exactly seven elements:

• the mode of achievement of the illocutionary point,
• the degree of strength of the illocutionary point,
• the propositional content conditions,
• the preparatory conditions,
• the sincerity conditions,
• the degree of strength of the sincerity conditions.

There are exactly five points according to Searle and Vanderveken (1985):

• The assertive point is to say how things are.
• The commissive point is to commit speaker to doing something.
• The directive point is to get other people to do things.
• The declarative point is to change the world by saying so.
• The expressive point is to express feelings and attitudes.

Later treatments of this matter tend to disregard much of the complexity of this earlier approach for the reason that it fails to have any predictive power. Especially difficult to handle are “strengths”, for example. Modern models try to use update models instead (see Section 2.3 below). Van der Sandt 1991 uses a discourse model with three different slates (for each speaker, and one common slate). While each speaker is responsible for maintaining his own slate, changes to the common slate can only be made through communication with each other. Merin 1994 seeks to reduce the manipulations to a sequential combination of so-called elementary social acts : claim , concession , denial , and retraction .

Uttering a sentence is acting. This action can have various consequences, partly intended partly not. The fact that utterances as actions are embedded in a bigger scheme of interaction between humans has been put into focus recently (see, for example, Clark 1996). Another important aspect that has been highlighted recently is the fact that by uttering a sentence we can change the knowledge state of an entire group of agents, see Balbiani et al. 2008. After publicly announcing \(\phi\), \(\phi\) becomes common knowledge among the entire group. This idea sheds new light on a problem of Gricean pragmatics, where certain speech acts can only be successful if certain facts are commonly known between speaker and hearer. It is by means of an utterance that a speaker can establish this common knowledge in case it wasn’t already there.

Davidson (1967) gave an account of action sentences in terms of what is now widely known as events . The basic idea is that an action sentence has the form \((\exists e)(\cdots)\), where \(e\) is a variable over acts. For example, “Brutus violently stabbed Caesar” is translated (ignoring tense) as \((\exists e) (\mathop{\mathrm{stab}}(e,\mathrm{Brutus},\mathrm{Caesar}) \wedge \mathop{\mathrm{violent}}(e))\). This allows to capture the fact that this sentence logically entails that Brutus stabbed Caesar. This idea has been widely adopted in linguistics; moreover, it is now assumed that basically all verbs denote events (Parsons 1990). Thus action sentences are those that speak about special types of events, called eventualities .

Vendler (1957) classified verbs into four groups:

• states (“know”, “sit”),
• activities (“run”, “eat”),
• accomplishments (“write a letter”, “build a house”), and
• achievements (“reach”, “arrive”).

Moens and Steedman (1988) add a fifth category:

• points (“flash”, “burst”).

The main dividing line is between states and the others. The types (b)–(e) all refer to change. This division has been heavily influential in linguistic theory; mostly, however, research concentrated on its relation to aspect. It is to be noted, for example, that verbs of type (c) can be used with the progressive while verbs of type (d) cannot. In an attempt to explain this, Krifka 1986 and Krifka 1992 have introduced the notion of an incremental theme . The idea is that any eventuality has an underlying activity whose progress can be measured using some underlying participant of the event. If, for example, I write a letter then the progress is measured in amounts of words. The letter is therefore the incremental theme in “I write a letter” since it defines the progress. One implementation of the idea is the theory of aspect by Verkuyl (1993). Another way to implement the idea of change is constituted by a translation into propositional dynamic logic (see Naumann 2001). Van Lambalgen and Hamm (2005) have applied the event calculus by Shanahan (1990) to the description of events.

The idea that propositions can not only be viewed as state descriptions but also as updates has been advocated independently by many people. Consider the possible states of an agent to be (in the simplest case) a theory (that is, a deductively closed set of sentences). Then the update of a theory \(T\) by a proposition \(\phi\) is the deductive closure of \(T \cup \left\{\phi\right\}\).

Gärdenfors 1988 advocates this perspective with particular attention to belief revision. Veltman 1985 develops the update view for the treatment of conditionals. One advantage of the idea is that it is possible to show why the mini discourse “It rains. It may not be raining.” is infelicitous in contrast to “It may not be raining. It rains.”. Given that an update is felicitous only to a consistent theory, and that “may \(\phi\)” (with epistemic “may”) simply means “it is consistent” (written \(\diamond\phi\)), the first is the sequence of updates with \(\phi\) and \(\diamond \neg\phi\). The second step leads to inconsistency, since \(\phi\) has already been added. It is vital in this approach that the context is constantly changing.

Heim 1983 contains an attempt to make this idea fruitful for the treatment of presuppositions. In Heim’s proposal, a sentence has the potential to change the context, and this is why, for example, the sentence “If John is married his wife will be happy.” does not presuppose that John is married. Namely, the second part of the conditional (“his wife will be happy”) is evaluated against the context incremented by the antecedent (“John is married”). This of course is the standard way conditions are evaluated in computer languages. This parallel is exploited in Van Eijck 1994, see also Kracht 1993.

The idea of going dynamic was further developed in Dynamic Predicate Logic (DPL, see Groenendijk and Stokhof 1991), where all expressions are interpreted dynamically. The specific insight in this grammar is that existential quantifiers have a dynamically growing scope. This has first been noted in Kamp 1981, where a semantics was given in terms of intermediate representations, so-called Discourse Representation Structures. Groenendijk and Stokhof replace these structures by introducing a dynamics into the evaluation of a formula, as proposed in Dynamic Logic (DL). An existential quantifier is translated as a random assignment “\(x \leftarrow\ ?\)” of DL, whose interpretation is a relation between assignments: it is the set of pairs \(\langle \beta ,\gamma \rangle\) such that \(\beta(y) = \gamma(y)\) for all \(y \ne x\) (in symbols \(\beta \sim_x \gamma\)). The translation of the sentence “A man walks.” is

• (1) \(\langle x \leftarrow ?\rangle \mathop{\mathrm{man}}'(x) \wedge \mathop{\mathrm{walk}}'(x)\)

This is a proposition, hence interpreted as a set. One can however, push the dynamicity even lower, and make all meanings relational. Then “A man walks.” is interpreted by the ‘program’

• (2) \(x \leftarrow ?; \mathop{\mathrm{man}}'(x)?; \mathop{\mathrm{walk}}'(x)?\)

Here, \(\mathop{\mathrm{man}}'(x)?\) uses the test constructor “\(?\)”: \(\phi ?\) is the set of all \(\langle \beta ,\beta \rangle\) such that \(\beta\) satisfies \(\phi\). The meaning of the entire program (2) therefore also is a relation between assignments. Namely, it is the set \(R\) of all pairs \(\langle \beta ,\gamma \rangle\) where \(\beta \sim_x \gamma\), and \(\gamma(x)\) walks and is a man. The meaning of (1) by contrast is the set of all \(\beta\) such that some \(\langle \beta ,\gamma \rangle \in R\). Existential quantifiers thus have ‘side effects’: the change in assignment is never undone by a quantifier over a different variable. Hence the open-endedness to the right of the existential. This explains the absence of brackets in (1). For an overview of dynamic semantics see Muskens et al. 1997.

3. The Logic of Action in Computer Science

The logic of action plays an important role in computer science. This becomes evident once one realizes that computers perform actions in the form of executing program statements written down in some programming language, changing computer internals and, by interfaces to the outside world, also that outside world. As such a logic of action provides a means to reason about programs, or more precisely, the execution of programs and their effects. This enables one to prove the correctness of programs. In principle, this is something very desirable: if we could prove all our software correct, we would know that they would function exactly the way we designed them. This was already realized by pioneers of computer programming such as Turing (1949) and Von Neumann (Goldstein and Von Neumann 1963). Of course, this ideal is too hard to establish in daily practice for all software. Verification is a nontrivial and time-consuming occupation, and there are also theoretical limitations to it. However, as the alternative is “just” massive testing of programs experimentally, with no 100% guarantee of correctness, it has remained an active area of research to this day.

Program verification has a long history. Already since the inception of the computer and its programming researchers started to think of ways of analyzing programs to be sure they did what they were supposed to do. In the 60s the development of a true mathematical theory of program correctness began to take serious shape (de Bakker 1980, 466). Remarkably, the work of John McCarthy who we will also encounter later on when we turn to the field of artificial intelligence played an important role here, distinguishing and studying fundamental notions such as ‘state’, McCarthy 1963a. This led on the one hand to the field of semantics of programming languages, and on the other to major advances in program correctness by Floyd (1967), Naur (1966), Hoare (1969) and Dijkstra (1976) (de Bakker 1980). Floyd and Naur used an elementary stepwise induction principle and predicates attached to program points to express invariant properties of imperative-style programs (Cousot 1990, 859), programs that are built up from basic assignment statements (of arithmetical expressions to program variables) and may be composed by sequencing, conditionals and repetitions. While the Floyd-Naur approach—called the inductive assertion method —giving rise to a systematic construction of verification conditions, was a method to prove the correctness of programs by means of logic, it was not a logic itself in the strict sense of the word. The way to a proper logic of programs was paved by Hoare, whose compositional proof method led to what is now known as Hoare logic. By exploiting the syntactic structure of (imperative-style) programs, Hoare was able to turn the Floyd-Naur method into a true logic with as assertions so-called Hoare triples of the form \(\left\{P\right\}S\left\{Q\right\}\), where \(P\) and \(Q\) are first-order formulas and \(S\) is a program statement in an imperative-style programming language as mentioned above. The intended reading is if \(P\) holds before execution of the statement \(S\) then \(Q\) holds upon termination of (execution of) \(S\). (The issue whether the execution of \(S\) terminates can be put in the reading of this Hoare triple either conditionally (partial correctness) or nonconditionally (total correctness), giving rise to different logics, see Harel et al. 2000). To give an impression of Hoare-style logics, we give here some rules for a simple programming language consisting of variable assignments to arithmetic expressions, and containing sequential (;), conditional \((\lif)\) and repetitive \((\lwhile)\) composition.

Later Pratt and Harel generalized Hoare logic to dynamic logic (Pratt 1976, Pratt 1979a, Harel 1979, Harel 1984, Kozen and Tiuryn 1990, Harel et al. 2000), of which it was realized [ 1 ] that it is in fact a form of modal logic, by viewing the input-output relation of a program \(S\) as an accessibility relation in the sense of Kripke-style semantics. [ 2 ] A Hoare triple \(\left\{P\right\}S\left\{Q\right\}\) becomes in dynamic logic the following formula: \(P \rightarrow[S] Q\), where [\(S\)] is the modal box operator associated with (the accessibility relation associated with) the input-output relation of program \(S\). The propositional version of Dynamic Logic, PDL, was introduced by Fischer and Ladner (1977), and became an important topic of research in itself. The key axiom of PDL is the induction axiom

where \(^*\) stands for the iteration operator, \(S^*\) denoting an arbitrary (finite) number of iterations of program \(S\). The axiom expresses that if after any number of iterations of \(S\) the truth of \(P\) is preserved by the execution of \(S\), then, if \(P\) is true at the current state, it will also be true after any number of iterations of \(S\). A weaker form of PDL, called HML, with only an atomic action box and diamond and propositional connectives, was introduced by Hennessy & Milner to reason about concurrent processes, and in particular analyze process equivalence (Hennessy and Milner 1980).

It is also worth mentioning here that the work of Dijkstra (1976) on weakest precondition calculus is very much related to dynamic logic (and Hoare’s logic). In fact, what Dijkstra calls the weakest liberal precondition , denoted \(\mathbf{wlp}(S,Q)\), is the same as the box operator in dynamic logic: \(\mathbf{wlp}(S,Q) = [S]Q\), while his weakest precondition , denoted \(\mathbf{wp}(S,Q)\), is the total correctness variant of this, meaning that this expression also entails the termination of statement \(S\) (Cousot 1990).

It was later realized that the application of dynamic logic goes beyond program verification or reasoning about programs. In fact, it constitutes a logic of general action. In Meyer 2000 a number of other applications of dynamic logic are given including deontic logic (see also Meyer 1988), reasoning about database updates, the semantics of reasoning systems such as reflective architectures. As an aside we note here that the use of dynamic logic for deontic logic as proposed in Meyer 1988 needed an extension of the action language, in particular the addition of the ‘action negation’ operator. The rather controversial nature of this operator triggered work on action negation in itself (see e.g. , Broersen 2004). Below we will also encounter the use of dynamic logic in artificial intelligence when specifying intelligent agents.

The logics thus far are adequate for reasoning about programs that are supposed to terminate and display a certain input/output behavior. However, in the late seventies one came to realize that there are also programs that are not of this kind. Reactive programs are designed to react to input streams that in theory may be infinite, and thus show ideally nonterminating behavior. Not so much input-output behavior is relevant here but rather the behavior of programs over time. Therefore Pnueli (1977) proposed a different way of reasoning about programs for this style of programming based on the idea of a logic of time, viz . ( linear-time ) temporal logic . (Since reactivity often involves concurrent or parallel programming, temporal logic is often associated with this style of programming. However, it should be noted that a line of research continued to extend the use of Hoare logic to concurrent programs (Lamport 1977, Cousot 1990, de Roever et al. 2001).) Linear-time temporal logic typically has temporal operators such as next-time, always (in the future), sometime (in the future), until and since.

An interesting difference between temporal logic on the one hand, and dynamic logic and Hoare logic on the other, is that the former is what in the literature is called an endogenous logic, while the latter are so-called exogenous logics. A logic is exogenous if programs are explicit in the logical language, while for endogenous logics this is not the case. In an endogenous logic such as temporal logic the program is assumed to be fixed, and is considered part of the structure over which the logic is interpreted (Harel et al. 2000, 157). Exogenous logics are compositional and have the advantage of allowing analysis by structural induction. Later Pratt (1979b) tried to blend temporal and dynamic logic into what he called process logic , which is an exogenous logic for reasoning about temporal behavior.

At the moment the field of temporal logic as applied in computer science has developed into a complete subfield on its own, including techniques and tools for (semi-)automatic reasoning and model-checking ( cf . Emerson 1990). Also variants of the basic linear-time models have been proposed for verification, such as branching-time temporal logic (and, in particular the logics CTL (computation tree logic) and its extension CTL* (Emerson 1990), in which one can reason explicitly about (quantification over) alternative paths in nondeterministic computations, and more recently also an extension of CTL, called alternating-time temporal logic (ATL), with a modality expressing that a group of agents has a joint strategy to ensure its argument, to reason about so-called open systems. These are systems, the behavior of which depends also on the behavior of their environments, see Alur et al. 1998.

Finally we mention still alternative logics to reason about programs, viz . fixpoint logics, with as typical example the so-called \(\mu\)-calculus, dating back to Scott and de Bakker (1969), and further developed in Hitchcock and Park 1972, Park 1976, de Bakker 1980, and Meyer 1985. The basic operator is the least fixed point operator \(\mu\), capturing iteration and recursion: if \(\phi(X)\) is a logical expression with a free relation variable \(X\), then the expression \(\mu X\phi(X)\) represents the least \(X\) such that \(\phi(X) = X\), if such an \(X\) exists. A propositional version of the \(\mu\)-calculus, called propositional or modal \(\mu\)-calculus comprising the propositional constructs \(\rightarrow\) and false , together with the atomic (action) modality [\(a\)] and \(\mu\) operator is completely axiomatized by propositional modal logic plus the axiom \(\phi[X/\mu X\phi] \rightarrow \mu X\phi\), where \(\phi[X/Y\)] stands for the expression \(\phi\) in which \(X\) is substituted by \(Y\), and rule

(Kozen 1983, Bradfield and Stirling 2007). This logic is known to subsume PDL ( cf . Harel et al. 2000).

4. The Logic of Action in Artificial Intelligence

In the field of artificial intelligence (AI), the aim is to devise intelligently behaving computer-based artifacts (with the purpose of understanding human intelligence or just making intelligent computer systems and programs). In order to achieve this, there is a tradition within AI to try and construct these systems based on symbolic representations of all relevant factors involved. This tradition is called symbolic AI or ‘good old-fashioned’ AI (GOFAI). In this tradition the sub-area of knowledge representation (KR) obviously is of major importance: it played an important role since the inception of AI, and has developed to a substantial field of its own. One of the prominent areas in KR concerns the representation of actions, performed by either the system to be devised itself or the actors in its environment. Of course, besides their pure representation also reasoning about actions is important, since representation and reasoning with these representations are deemed to be closely connected within KR (which is sometime also called KR&R, knowledge representation & reasoning). A related, more recent development within AI is that of basing the construction of intelligent systems on the concept of an ( intelligent ) agent , an autonomously acting entity, regarding which, by its very nature, logics of action play a crucial role in obtaining a logical description and specification.

As said above, the representation of actions and formalisms/logics to reason with them are very central to AI and particularly the field of KR. One of the main problems that one encounters in the literature on reasoning about actions in AI, and much more so than in mainstream computer science, is the discovery of the so-called frame problem (McCarthy and Hayes 1969). Although this problem has been generalized by philosophers such as Dennett (1984) to a general problem of relevance and salience of properties pertaining to action, the heart of the problem is that in a ‘common-sense’ setting as one encounters in AI, it is virtually impossible to specify all the effects by the actions of concern, as well as, notably, all non -effects. For instance, given an action, think about what changes if the action is performed and what does not—generally the latter is much more difficult to produce than the former, leading to large, complex attempts to specify the non-effects. But there is of course also the problem of relevance : what aspects are relevant for the problem at hand; which properties do we need to take into consideration? In particular, this also pertains to the preconditions of an action that would guarantee the successful performance/execution of an action. Again, in a common-sense environment, these are formidable, and one can always think of another (pre)condition that should be incorporated. For instance, for successfully starting the motor of a car, there should be a charged battery, sufficient fuel, …, but also not too cold weather, or even sufficient power in your fingers to be able to turn the starting key, the presence of a motor in the car, … etc. etc. In AI one tries to find a solution for the frame problem, having to do with the smallest possible specification. Although this problem gave rise to so-called defeasible or non-monotonic solutions such as defaults (‘normally a car has a motor’), which in itself gave rise to a whole new a realm within AI called nonmonotonic or commonsense reasoning , this is beyond the scope of this entry (we refer the interested reader to the article by Thomason (2003) in this encyclopedia). We focus here on a solution that does not appeal to nonmonotonicity (directly).

Reiter (2001) has proposed a (partial) solution within a framework, called the situation calculus , that has been very popular in KR especially in North America since it was proposed by John McCarthy, one of the founding fathers of AI (McCarthy 1963b, McCarthy 1986). The situation calculus is a dialect of first-order logic with some mild second-order features, especially designed to reason about actions. (One of its distinctive features is that of the so-called reification of semantic notions such as states or possible worlds (as well as truth predicates) into syntactic entities (‘situations’) in the object language.) For the sake of conformity in this entry and reasons of space, we will try rendering Reiter’s idea within (first-order) dynamic logic, or rather, a slight extension of it. (We need action variables to denote action expressions and equalities between action variables and actions (or rather action expressions) as well as (universal) quantification over action variables).

What is known as Reiter’s solution to the frame problem assumes a so-called closed system, that is to say, a system in which all (relevant) actions and changeable properties (in this setting often called ‘fluents’ to emphasize their changeability over time) are known. By this assumption it is possible to express the (non)change as a consequence of performing actions as well as the issue of the check for the preconditions to ensure successful performance in a very succinct and elegant manner, and coin it in a so-called successor state axiom of the form

where \(A\) is an action variable, and \(\gamma_{f}^+ (\boldsymbol{x}, A)\) and \(\gamma_{f}^- (\boldsymbol{x}, A)\) are ‘simple’ expressions without action modalities expressing the conditions for \(\phi\) becoming true and false, respectively. So the formula is read informally as, under certain preconditions pertaining to the action \(A\) at hand, the fluent (predicate) \(f\) becomes true of arguments \(\boldsymbol{x}\), if and only if either the condition \(\gamma_{f}^+ (\boldsymbol{x}, A)\) holds or \(f(\boldsymbol{x})\) holds (before the execution of \(A)\) and the condition \(\gamma_{f}^- (\boldsymbol{x}, A)\) (that would cause it to become false) does not hold. Furthermore, the expression \(\Poss(A)\) is used schematically in such axioms, where the whole action theory should be complemented with so-called precondition axioms of the form \(\phi_A \rightarrow \Poss(A)\) for concrete expressions \(\phi_A\) stating the actual preconditions needed for a successful execution of \(A\).

To see how this works out in practice we consider a little example in a domain where we have a vase \(v\) which may be broken or not (so we have “broken” as a fluent), and actions drop and repair. We also assume the (non-changeable) predicates fragile and held-in-hand of an object. Now the successor state axiom becomes

and as precondition axioms we have \(\textrm{held-in-hand}(x) \rightarrow \Poss(\mathop{\mathrm{drop}}(x))\) and \(\mathop{\mathrm{broken}}(x) \rightarrow \Poss(\mathop{\mathrm{repair}}(x))\). This action theory is very succinct: one needs only one successor state axiom per fluent and one precondition axiom per action.

Finally in this subsection we must mention some other well-known approaches to reasoning about action and change. The event calculus (Kowalski and Sergot 1986, Shanahan 1990, Shanahan 1995) and fluent calculus (Thielscher 2005) are alternatives to the situation-based representation of actions in the situation calculus. The reader is also referred to Sandewall and Shoham 1994 for historical and methodological issues as well as the relation with non-monotonic reasoning. These ideas have led to very efficient planning systems ( e.g. , TALplanner, Kvarnström and Doherty 2000) and practical ways to program robotic agents ( e.g. , the GOLOG family (Reiter 2001) of languages based on the situation calculus, and FLUX (Thielscher 2005) based on the fluent calculus).

In the last two decades the notion of an intelligent agent has emerged as a unifying concept to discuss the theory and practice of artificial intelligence ( cf . Russell and Norvig 1995, Nilsson 1998). In short, agents are software entities that display forms of intelligence/rationality and autonomy. They are able to take initiative and make decisions to take action on their own without direct control of a human user. In this subsection we will see how logic (of action) is used to describe / specify the (desired) behavior of agents ( cf . Wooldridge 2002). First we focus on single agents, after which we turn to settings with multiple agents, called multi-agent systems (MAS) or even agent societies.

4.2.1 Single agent approaches

Interestingly, the origin of the intelligent agent concept lies in philosophy.

First of all there is a direct link with practical reasoning in the classical philosophical tradition going back to Aristotle. Here one is concerned with reasoning about action in a syllogistic manner, such as the following example taken from Audi 1999, p. 729:

Would that I exercise. Jogging is exercise. Therefore, I shall go jogging.

Although this has the form of a deductive syllogism in the familiar Aristotelian tradition of theoretical reasoning, on closer inspection it appears that this syllogism does not express a purely logical deduction. (The conclusion does not follow logically from the premises.) It rather constitutes a representation of a decision of the agent (going to jog), where this decision is based on mental attitudes of the agent, viz . his/her beliefs (jogging is exercise) and his/her desires or goals (would that I exercise). So, practical reasoning is reasoning directed toward action, the process of figuring out what to do, as Wooldridge (2000) puts it. The process of reasoning about what to do next on the basis of mental states such as beliefs and desires is called deliberation.

Dennett (1971) has put forward the notion of the intentional stance : the strategy of interpreting the behaviour of an entity by treating it as if it were a rational agent that governed its choice of action by a consideration of its beliefs and desires. As such it is an anthropomorphic instance of the so called design (functionality) stance, contra the physical stance, towards systems. This stance has been proved to be extremely influential, not only in cognitive science and biology/ethology (in connection with animal behavior), but also as a starting point of thinking about artificial agents.

Finally, and most importantly, there is the work of the philosopher Michael Bratman (1987), which, although in the first instance aimed at human agents, lays the foundation of the BDI approach to artificial agents. In particular, Bratman makes a case for the incorporation of the notion of intention for describing agent behavior. Intentions play the important role of selection of actions that are desired, with a distinct commitment attached to the actions thus selected. Unless there is a rationale for dropping a commitment (such as the belief that an intention has been achieved already or the belief that it is impossible to achieve) the agent should persist / persevere in its commitment, stick to it, so to speak, and try realizing it,

After Bratman’s philosophy was published, researchers tried to formalize this theory using logical means. We mention here three well-known approaches. Cohen and Levesque (1991) tried to capture Bratman’s theory in a linear-time style temporal logic where they added primitive operators for belief and goal as well as some operators to cater for actions, such as operators for expressing that an action is about to be performed \((\lhappens \alpha)\), has just been performed \(\ldone \alpha)\) and what agent is the actor of a primitive action (\(\lact i\ \alpha\): agent \(i\) is the actor of \(\alpha\)). From this basic set-up they build a framework in which ultimately the notion of intention is defined in terms of the other notions. In fact they define two notions: an intention_to_do and an intention_to_be. First they define the notion of an achievement goal (A-Goal): an A-Goal is something that is a goal to hold later, but is believed not to be true now. Then they define a persistent goal (P-Goal): a P-Goal is an A-Goal that is not dropped before it is believed to be achieved or believed to be impossible. Then the intention to do an action is defined as the P-Goal of having done the action, in a way such that the agent was aware of it happening. The intention to achieve a state satisfying \(\phi\) is the P-Goal of having done some action that has \(\phi\) as a result where the agent was aware of something happening leading to \(\phi\), such that what actually happened was not something that the agent explicitly had not as a goal.

Next there is Rao & Georgeff’s formalization of BDI agents using the branching-time temporal logic CTL (Rao and Georgeff 1991, Rao and Georgeff 1998, Wooldridge 2000). On top of CTL they introduce modal operators for Belief \((\lbel)\), Goal \((\lgoal)\) (sometimes replaced by Desire \((\ldes)\)) and Intention (of the to_be kind, \(\lintend\)) as well as operators to talk about the success \((\lsucceeded(e))\) and failure \((\lfailed)\) of elementary actions \(e\). So they do not try to define intention in terms of other notions, but rather introduce intention as a separate operator, of which the meaning is later constrained by ‘reasonable’ axioms. The formal semantics is based on Kripke models with accessibility relations between worlds for the belief, goal and intention operators. However, possible worlds here are complete time trees (modeling the various behaviors of the agent) on which CTL formulas are interpreted in the usual way. Next they propose a number of postulates/axioms that they find reasonable interactions between the operators, and constrain the models of the logic accordingly so that these axioms become validities. For example, they propose the formulas \(\lgoal(\alpha) \rightarrow \lbel(\alpha)\) and \(\lintend(\alpha) \rightarrow \lgoal(\alpha)\), for a certain class of formulas \(\alpha\), of which \(\alpha = \mathop{\mathbf{E}}(\psi)\) is a typical example. Here \(\mathop{\mathbf{E}}\) stands for the existential path quantifier in CTL. Rao and Georgeff also show that one can express commitment strategies in their logic. For example, the following expresses a ‘single-minded committed’ agent, that keeps committed to its intention until it believes it has achieved it or thinks it is impossible (which is very close to what we saw in the definition of intention in the approach of Cohen and Levesque):

where \(\mathbf{A}\) stands for the universal path quantifier in CTL.

Finally there is the KARO approach by Van Linder et al. (Van der Hoek et al. 1998, Meyer et al. 1999), which takes dynamic logic as a basis instead of a temporal logic. First a core is built, consisting of the language of propositional dynamic logic augmented with modal operators for knowledge \((\mathbf{K})\), belief \((\mathbf{B})\) and desire \((\mathbf{D})\) as well as an operator \((\mathbf{A})\) that stands for ability to perform an action. Next the language is extended mostly by abbreviations (definitions in terms of the other operators) to get a fully-fledged BDI-like logic. The most prominent operators are:

• opportunity to do an action (meaning that there is a way the action can be executed leading to a next state)
• practical possibility to do an action with respect to an assertion (the conjunction of ability and opportunity of doing the action, together with the statement that the execution of the action leads to the truth of the assertion)
• can do an action with respect to an assertion (knowing to have the practical possibility to do the action with respect to the assertion at hand)
• realizability of an assertion (the existence of a plan, i.e. a sequence of atomic actions, which the agent has the practical possibility to perform with respect to the assertion at hand)
• goal with respect to an assertion (the conjunction of the assertion being desirable, not true yet, but realizable)
• possibly intend to do an action with respect to an assertion (expressing that the agent can do the action with respect to the assertion of which he knows it to be a goal of his)

The framework furthermore has special actions \(\lcommit\) and \(\luncommit\) to control the agent’s commitments. The semantics of these actions is such that the agent obviously can only commit to an action \(\alpha\) if there is good reason for it, viz . that there is a possible intention of \(\alpha\) with a known goal \(\phi\) as result. Furthermore the agent cannot uncommit to a certain action \(\alpha\) that is part of the agent’s commitments, as long there is a good reason for it to be committed to \(\alpha\), i.e. as long as there is some possible intention where \(\alpha\) is involved. This results in having the following validities in KARO: (Here \(\mathbf{I}(\alpha, \phi)\) denotes the possibly intend operator and \(\mathbf{Com}(\alpha)\) is an operator that expresses that the agent is committed to the action \(\alpha\), which is similar to Cohen & Levesque’s intention-to-do operator \(\lintend_1\) in Cohen and Levesque 1990.)

Informally these axioms say the following: if the agent possibly intends an action for fulfilling a certain goal then it has the opportunity to commit to this action, after which it is recorded on its agenda; as long as an agent possibly intends an action it is not able to uncommit to it (this reflects a form of persistence of commitments: as long as there is a good reason for a plan on the agenda it will have to stay on!); if the agent is committed to an action it has the opportunity to uncommit to it (but it may lack the ability to do this, cf . the previous axiom); if an agent is committed to a sequence of two actions then it knows that it is committed to the first and it also knows that after performing the first action it will be committed to the second.

Besides this focus on motivational attitudes in the tradition of agent logics in BDI style, the KARO framework also provides an extensive account of epistemic and doxastic attitudes. This is worked out most completely in Van Linder et al. 1995. This work hooks into a different strand of research in between artificial intelligence and philosophy, viz . Dynamic Epistemic Logic , the roots of which lie in philosophy, linguistics, computer science and artificial intelligence! Dynamic Epistemic Logic (DEL) is the logic of knowledge change; it is not about one particular logical system, but about a whole family of logics that allow us to specify static and dynamic aspects of knowledge and beliefs of agents ( cf . Van Ditmarsch et al. 2007). The field combines insights from philosophy (about belief revision, AGM-style (AGM 1985), as we have seen in Section 1), dynamic semantics in linguistics and the philosophy of language (as we have seen in Section 2), reasoning about programs by using dynamic logic (as we have seen in Section 3) with ideas in artificial intelligence about how knowledge and actions influence each other (Moore 1977). More generally we can see the influence of the logical analysis of information change as advocated by van Benthem and colleagues (van Benthem 1989, van Benthem 1994, Faller et al. 2000). Also Veltman’s update semantics of default reasoning (Veltman 1996), an important reasoning method in artificial intelligence (Reiter 1980, Russell and Norvig 1995), can be viewed as being part of this paradigm.

For the purpose of this entry, it is interesting to note that the general approach taken is to apply a logic of action, viz . dynamic logic, to model information change. This amounts to an approach in which the epistemic (or doxastic) updates are reified into the logic as actions that change the epistemic/doxastic state of the agent. So, for example in Van Linder et al. 1995 we encounter the actions such as \(\lexpand(\phi)\), \(\lcontract(\phi)\), \(\lrevise(\phi)\), referring to expanding, contracting and revising, respectively, one’s belief with the formula \(\phi\). These can be reasoned about by putting them in dynamic logic boxes and diamonds, so that basically extensions of dynamic logic are employed for reasoning about these updates. It is further shown that these actions satisfy the AGM postulates so that this approach can be viewed as a modal counterpart of the AGM framework. Very similar in spirit is the work of Segerberg (1995) on Dynamic Doxastic Logic (DDL), the modal logic of belief change. In DDL modal operators of the form [\(+\phi\)], [*\(\phi\)] and [\(-\phi\)] are introduced with informal meanings: “after the agent has expanded/revised/contracted his beliefs by \(\phi\)”, respectively. Combined with the ‘standard’ doxastic operator \(B\), where \(B\phi\) is interpreted as “\(\phi\) is in the agent’s belief set”, one can now express properties like [\(+\phi]B\psi\) expressing that after having expanded its beliefs by \(\phi\) the agent believes \(\psi\) (also cf . Hendricks and Symons 2006).

Finally in this subsection we mention recent work where the KARO formalism is used as a basis for describing also other aspects of cognitive behavior of agents, going ‘beyond BDI’, viz . attitudes regarding emotions (Meyer 2006, Steunebrink et al. 2007, Steunebrink et al. 2012). The upshot of this approach is that an expressive logic of action such as KARO can be fruitfully employed to describe how emotions such as joy, gratification, anger, and remorse, are triggered by certain informational and motivational attitudes such as certain beliefs and goals (‘emotion elicitation’) and how, once elicited, the emotional state of an agent may influence its behavior, and in particular its decisions about the next action to take.

4.2.2 Multi-agent approaches

Apart from logics to specify attitudes of single agents, also work has been done to describe the attitudes of multi-agent systems as wholes. First we mention the work by Cohen & Levesque in this direction (Levesque et al. 1990, Cohen and Levesque 1991). This work was a major influence on a multi-agent version of KARO (Aldewereld et al. 2004). An important complication in a notion of joint goal involves that of persistence of the goal: where in the single agent case the agent pursues its goal until it believes it has achieved it or believes it can never be achieved, in the context of multiple agents, the agent that realizes this, has to inform the others of the team about it so that the group/team as a whole will believe that this is the case and may drop the goal. This is captured in the approaches mentioned above. Related work, but not a logic of action in the strict sense, concerns the logical treatment of collective intentions (Keplicz and Verbrugge 2002).

It must also be mentioned here that inspired by several sources among which the work on knowledge and belief updates for individual agents as described by DEL and DDL, combined with work on knowledge in groups of agents such as common knowledge (see, e.g., Meyer and Van der Hoek 1995), a whole new subfield has arisen, which can be seen as the multi-agent (counter)part of Dynamic Epistemic Logic. This deals with matters such as the logic of public announcement, and more generally actions that have effect on the knowledge of groups of agents. This has generated quite some work by different authors such as Plaza (1989), Baltag (1999), Gerbrandy (1998), Van Ditmarsch (2000), and Kooi (2003). For example, public announcement logic (Plaza 1989) contains an operator of the form [\(\phi]\psi\), where both \(\phi\) and \(\psi\) are formulas of the logic, expressing “after announcement of \(\phi\), it holds that \(\psi\)”. This logic can be seen as a form of dynamic logic again, where the semantic clause for [\(\phi]\psi\) reads (in informal terms): [\(\phi]\psi\) is true in a model-state pair iff the truth of \(\phi\) in that model-state pair implies the truth of \(\psi\) in a model-state pair, where the state is the same, but the model is transformed to capture the information contained in \(\phi\). Also in the other approaches the transformation of models induced by communicated information plays an important role, notably in the approach by Baltag et al. on action models (Baltag 1999, Baltag and Moss 2004). A typical element in this approach is that in action model logic one has both epistemic and action models and that the update of an epistemic model by an epistemic action (an action that affects the epistemic state of a group of agents) is represented by a (restricted) modal product of that epistemic model and an action model associated with that action. (See Van Ditmarsch et al. 2007, p. 151; this book is a recent comprehensive reference to the field.)

Finally we mention logics that incorporate notions from game theory to reason about multi-agent systems, such as game logic, coalition logic (Pauly 2001) and alternating temporal logic (ATL, which we also encountered at the end of the section on mainstream computer science!), and its epistemic variant ATEL (Van der Hoek and Wooldridge 2003, Van der Hoek et al. 2007). For instance, game logic is an extension of PDL to reason about so-called determined 2-player games. Interestingly there is a connection between these logics and the stit approach we have encountered in philosophy. For instance, Broersen, partially jointly with Herzig and Troquard, has shown several connections such as embeddings of Coalition Logic and ATL in forms of stit logic (Broersen et al. 2006a,b) and extensions of stit (and ATL) to cater for reasoning about interesting properties of multi-agent systems (Broersen 2009, 2010). This area currently is growing fast, also aimed at the application of verifying multi-agent systems ( cf . Van der Hoek et al. 2007), viz. Dastani et al. 2010. The latter constitutes still somewhat of a holy grail in agent technology. On the one hand there are many logics to reason about both single and multiple agents, while on the other hand multi-agent systems are being built that need to be verified. To this day there is still a gap between theory and practice. Much work is being done to render logical means combining the agent logics discussed and the logical techniques from mainstream computer science for the verification of distributed systems (from section 3), but we are not there yet…!

In this entry we have briefly reviewed the history of the logic of action, in philosophy, in linguistics, in computer science and in artificial intelligence. Although the ideas and techniques we have considered were developed in these separate communities in a quite independent way, we feel that they are nevertheless very much related, and by putting them together in this entry we hope we have contributed in a modest way to some cross-fertilization between these communities regarding this interesting and important subject.

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Educational resources and simple solutions for your research journey

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

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

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

Table of Contents

What is a hypothesis ?  

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

What is a research hypothesis ?  

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

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

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

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

How to create an effective research hypothesis  

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

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

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

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

How to write a research hypothesis  

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

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

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

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

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

Research hypothesis checklist  

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

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

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

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

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

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

2. Alternative hypothesis:

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

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

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

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

4. Non-directional hypothesis:

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

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

5. Simple hypothesis :

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

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

6 . Complex hypothesis :

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

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

7. Associative hypothesis:  

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

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

8 . Causal hypothesis:

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

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

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

Research hypothesis examples  

Here are some good research hypothesis examples :  

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

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

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

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

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

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

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

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

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

Importance of testable hypothesis  

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

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

Frequently Asked Questions (FAQs) on research hypothesis  

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

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

2. When to reject null hypothesis ?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

9. Can research hypotheses be used in qualitative research?

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

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• Published: 01 September 2001

Neurophysiological mechanisms underlying the understanding and imitation of action

• Giacomo Rizzolatti 1 , 2 ,
• Leonardo Fogassi 1 &
• Vittorio Gallese 1  

Nature Reviews Neuroscience volume  2 ,  pages 661–670 ( 2001 ) Cite this article

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What are the neural bases of action understanding? Although this capacity could merely involve visual analysis of the action, it has been argued that we actually map this visual information onto its motor representation in our nervous system. Here we discuss evidence for the existence of a system, the 'mirror system', that seems to serve this mapping function in primates and humans, and explore its implications for the understanding and imitation of action.

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A sensory–motor theory of the neocortex

Delineating visual, auditory and motor regions in the human brain with functional neuroimaging: a BrainMap-based meta-analytic synthesis

Action execution and action observation elicit mirror responses with the same temporal profile in human SII

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Also known as the Hoffmann reflex, the H reflex results from the stimulation of sensory fibres, which causes an excitatory potential in the motor neuron pool after a synaptic delay. Exceeding the potential threshold for a given motor neuron generates an action potential. The resulting discharge will cause the muscle fibres innervated by that neurone to be activated.

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Rizzolatti, G., Fogassi, L. & Gallese, V. Neurophysiological mechanisms underlying the understanding and imitation of action. Nat Rev Neurosci 2 , 661–670 (2001). https://doi.org/10.1038/35090060

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

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

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

At a Glance

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

The Hypothesis in the Scientific Method

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

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

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

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

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

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

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

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

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

The Importance of Operational Definitions

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

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

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

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

Replicability

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

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

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

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

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

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

Examples of an alternative hypothesis:

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

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

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

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

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

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

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

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

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

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

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The Craft of Writing a Strong Hypothesis

Table of Contents

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

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

What is a Hypothesis?

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

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

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

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

Different Types of Hypotheses‌

Types of hypotheses

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

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

1. Null hypothesis

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

2. Alternative hypothesis

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

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

3. Simple hypothesis

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

4. Complex hypothesis

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

5. Associative and casual hypothesis

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

6. Empirical hypothesis

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

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

7. Statistical hypothesis

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

Characteristics of a Good Hypothesis

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

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

Separating a Hypothesis from a Prediction

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

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

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

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

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

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

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

2. Carry out a recce

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

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

3. Create a 3-dimensional hypothesis

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

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

4. Write the first draft

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

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

5. Proof your hypothesis

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

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

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

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

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

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

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

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

2. What is an example of hypothesis?

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

3. What is an example of null hypothesis?

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

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

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

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

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

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

7. Difference between research question and research hypothesis?

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

8. What is plural for hypothesis?

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

9. What is the red queen hypothesis?

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

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

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

11. When to reject null hypothesis?

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

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What Is a Hypothesis and How Do I Write One?

General Education

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

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

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

So let’s get started!

What Is a Hypothesis?

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

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

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

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

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

Independent and Dependent Variables 

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

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

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

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

Elements of a Good Hypothesis

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

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

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

Writing Your Hypothesis

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

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

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

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

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

The Two Types of Hypotheses

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

#1: If-Then Hypotheses

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

If ____ happens, then ____ will happen.

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

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

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

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

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

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

#2: Null Hypotheses

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

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

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

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

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

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

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

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

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

4 Tips to Write the Best Hypothesis

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

#1: Plausibility

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

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

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

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

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

#2: Defined Concepts

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

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

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

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

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

Good hypotheses ensure that you can observe the results. 

#3: Observability

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

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

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

#4: Generalizability 

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

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

Hypothesis Testing Examples

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

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

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

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

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

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

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

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

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

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

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

Here’s what your hypotheses might look like: 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Key Takeaways: Hypothesis Writing

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

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

What’s Next?

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

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

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

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

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Action potential

Author: Jana Vasković, MD • Reviewer: Francesca Salvador, MSc Last reviewed: November 03, 2023 Reading time: 12 minutes

For a long time, the process of communication between the nerves and their target tissues was a big unknown for physiologists. With the development of electrophysiology and the discovery of electrical activity of neurons, it was discovered that the transmission of signals from neurons to their target tissues is mediated by action potentials. 

An action potential is defined as a sudden, fast, transitory, and propagating change of the resting membrane potential . Only neurons and muscle cells are capable of generating an action potential; that property is called the excitability .

This article will discuss the definition, steps and phases of the action potential.

Refractory period

Propagation of action potential.

Action potentials are nerve signals. Neurons generate and conduct these signals along their processes in order to transmit them to the target tissues. Upon stimulation, they will either be stimulated, inhibited, or modulated in some way. 

Learn the structure and the types of the neurons with the following study unit. 

But what causes the action potential? From an electrical aspect, it is caused by a stimulus with certain value expressed in millivolts [mV]. Not all stimuli can cause an action potential. Adequate stimulus must have a sufficient electrocal value which will reduce the negativity of the nerve cell to the threshold of the action potential. In this manner, there are subthreshold, threshold, and suprathreshold stimuli. Subthreshold stimuli cannot cause an action potential. Threshold stimuli are of enough energy or potential to produce an action potential (nerve impulse). Suprathreshold stimuli also produce an action potential, but their strength is higher than the threshold stimuli. 

So, an action potential is generated when a stimulus changes the membrane potential to the values of threshold potential . The threshold potential is usually around -50 to -55 mV. It is important to know that the action potential behaves upon the all-or-none law . This means that any subthreshold stimulus will cause nothing, while threshold and suprathreshold stimuli produce a full response of the excitable cell. 

Is an action potential different depending on whether it’s caused by threshold or suprathreshold potential? The answer is no. The length and amplitude of an action potential are always the same. However, increasing the stimulus strength causes an increase in the frequency of an action potential. An action potential propagates along the nerve fiber without decreasing or weakening of amplitude and length. In addition, after one action potential is generated, neurons become refractory to stimuli for a certain period of time in which they cannot generate another action potential.

From the aspect of ions, an action potential is caused by temporary changes in membrane permeability for diffusible ions. These changes cause ion channels to open and the ions to decrease their concentration gradients. The value of threshold potential depends on the membrane permeability, intra- and extracellular concentration of ions, and the properties of the cell membrane. 

An action potential has three  phases:  depolarization, overshoot, repolarization. There are two more states of the membrane potential related to the action potential. The first one is hypopolarization which precedes the depolarization, while the second one is hyperpolarization , which follows the repolarization.

Hypopolarization is the initial increase of the membrane potential to the value of the threshold potential. The threshold potential opens voltage-gated sodium channels and causes a large influx of sodium ions. This phase is called the depolarization . During depolarization, the inside of the cell becomes more and more electropositive, until the potential gets closer the electrochemical equilibrium for sodium of +61 mV. This phase of extreme positivity is the overshoot phase.

After the overshoot, the sodium permeability suddenly decreases due to the closing of its channels. The overshoot value of the cell potential opens voltage-gated potassium channels, which causes a large potassium efflux, decreasing the cell’s electropositivity. This phase is the repolarization phase, whose purpose is to restore the resting membrane potential. Repolarization always leads first to hyperpolarization , a state in which the membrane potential is more negative than the default membrane potential. But soon after that, the membrane establishes again the values of membrane potential.

After reviewing the roles of ions, we can now define the threshold potential more precisely as the value of the membrane potential at which the voltage-gated sodium channels open. In excitable tissues, the threshold potential is around 10 to 15 mV less than the resting membrane potential.

The refractory period is the time after an action potential is generated, during which the excitable cell cannot produce another action potential. There are two subphases of this period, absolute and relative refractoriness. 

Absolute refractoriness overlaps the depolarization and around 2/3 of repolarization phase. A new action potential cannot be generated during depolarization because all the voltage-gated sodium channels are already opened or being opened at their maximum speed. During early repolarization, a new action potential is impossible since the sodium channels are inactive and need the resting potential to be in a closed state, from which they can be in an open state once again. Absolute refractoriness ends when enough sodium channels recover from their inactive state.

Relative refractoriness is the period when the generation of a new action potential is possible, but only upon a suprathreshold stimulus. This period overlaps the final 1/3 of repolarization. 

An action potential is generated in the body of the neuron and propagated through its axon . Propagation doesn’t decrease or affect the quality of the action potential in any way, so that the target tissue gets the same impulse no matter how far they are from neuronal body.

The action potential generates at one spot of the cell membrane. It propagates along the membrane with every next part of the membrane being sequentially depolarized. This means that the action potential doesn’t move but rather causes a new action potential of the adjacent segment of the neuronal membrane.

We need to emphasize that the action potential always propagates forward , never backwards. This is due to the refractoriness of the parts of the membrane that were already depolarized, so that the only possible direction of propagation is forward. Because of this, an action potential always propagates from the neuronal body, through the axon to the target tissue.

The speed of propagation largely depends on the thickness of the axon and whether it’s myelinated or not. The larger the diameter, the higher the speed of propagation. The propagation is also faster if an axon is myelinated. Myelin increases the propagation speed because it increases the thickness of the fiber. In addition, myelin enables saltatory conduction of the action potential, since only the Ranvier nodes depolarize, and myelin nodes are jumped over.  In unmyelinated fibers, every part of the axonal membrane needs to undergo depolarization, making the propagation significantly slower. 

Do you want to learn faster all the parts and the functions of the nervous system? Go to our nervous system quiz article and ace your next exam. 

A synapse is a junction between the nerve cell and its target tissue. In humans, synapses are chemical , meaning that the nerve impulse is transmitted from the axon ending to the target tissue by the chemical substances called neurotransmitters (ligands). If a neurotransmitter stimulates the target cell to an action, then it is an excitatory neurotransmitter. On the other hand, if it inhibits the target cell, it is an inhibitory neurotransmitter.

Depending on the type of target tissue, there are central and peripheral synapses. Central synapses are between two neurons in the central nervous system, while peripheral synapses occur between a neuron and muscle fiber, peripheral nerve, or gland.

Each synapse consists of the:

• Presynaptic membrane – membrane of the terminal button of the nerve fiber 
• Postsynaptic membrane – membrane of the target cell 
• Synaptic cleft – a gap between the presynaptic and postsynaptic membranes

Inside the terminal button of the nerve fiber are produced and stored numerous vesicles that contain neurotransmitters. When the presynaptic membrane is depolarized by an action potential, the calcium voltage-gated channels open. This leads to an influx of calcium, which changes the state of certain membrane proteins in the presynaptic membrane, and results with exocitosis of the neurotransmitter in the synaptic cleft.

The postsynaptic membrane contains receptors for the neurotransmitters. Once the neurotransmitter binds to the receptor, the ligand-gated channels of the postsynaptic membrane either open or close. These ligand-gated channels are the ion channels , and their opening or closing will cause a redistribution of ions in the postsynaptic cell. Depending on whether the neurotransmitter is excitatory or inhibitory, this will result with different responses. 

Learn the types of the neurons with the following quiz. 

An action potential is caused by either threshold or suprathreshold stimuli upon a neuron. It consists of three phases: depolarization, overshoot, and repolarization.

An action potential propagates along the cell membrane of an axon until it reaches the terminal button. Once the terminal button is depolarized, it releases a neurotransmitter into the synaptic cleft. The neurotransmitter binds to its receptors on the postsynaptic membrane of the target cell, causing its response either in terms of stimulation or inhibition.

Action potentials are propagated faster through the thicker and myelinated axons, rather than through the thin and unmyelinated axons. After one action potential is generated, a neuron is unable to generate a new one due to its refractoriness to stimuli.

References:

• Hall, J. E., Guyton, A. C. (2011). Textbook of Medical Physiology (12th ed.). Philadelphia, PA: Saunders Elsevier.
• Moore, K. L., Dalley, A. F., & Agur, A. M. R. (2014). Clinically Oriented Anatomy (7th ed.). Philadelphia, PA: Lippincott Williams & Wilkins.
• Ross, M. J., Pawlina, W. (2011). Histology (6th ed.). Philadelphia, PA: Lippincott Williams & Wilkins.
• Patestas, M. A., Gartner, L. P. (2006). A Textbook of Neuroanatomy. Victoria, Australia: Blackwell Publishing Ltd.

Article, review and layout:

• Jana Vasković
• Francesca Salvador

Illustrations:

• Types of neurons and synapse (diagram) - Paul Kim
• Action potential curve (diagram) - Cristina Sala Ripoll
• Ions exchange in action potential (diagram) - Cristina Sala Ripoll

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