what makes a good hypothesis in geography

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Hypotheses: types, levels and functions | scientific method | geography.

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In this article we will discuss about:- 1. Types of Hypotheses 2. Levels of Hypothesis 3. Functions 4. Testing.

Types of Hypotheses :

There are several different kinds of hypotheses used in social and/or geographical analysis, studies and research.

However, the primary types of hypotheses are:

(1) Research Hypotheses,

(2) Null Hypotheses,

(3) Scientific Hypotheses, and

(4) Statistical Hypotheses.

1. Research Hypotheses:

Hypotheses derived from the researcher’s theory about some social and/or geographical phenomena are called research hypotheses or ‘working’ hypotheses.

The social investigator usually believes that his/her research hypotheses are true or they are accurate statements about the condition of things he/she is investigating. The investigator believes that these hypotheses are true to the extent that the theory from which they were derived is adequate.

Theories are, in one sense, suppositions about the true nature of things, and thus regarded as tentative statements about reality. Until they have been verified to the scientist’s satisfaction, the hypotheses derived from theories must also be regarded as tentative suppositions about things until they have been tested. Testing hypothesis means to subject it to confirmation or disconfirmation.

2. Null Hypotheses:

Null hypotheses are, in a sense, the reverse of research hypotheses. They are also statements about the reality of things, except that they serve to refute or deny what is explicitly indicated in a given research hypothesis.

Null hypotheses are hypothetical models used to test research hypotheses. The question that arises as why does the social investigator want to bother with so-called null hypotheses? Why doesn’t the investigator test the hypothesis directly and let it go at that?

These questions have been asked time and again by every researcher confronting null hypotheses for the first time. There are at least four explanations why null hypothesis models are used, none of which, however, may answer this question satisfactorily.

i. Trying to show the truthfulness of research hypotheses would imply to some, at least, a definite bias towards trying to confirm one’s suppositions and possibly ignoring those things that would tend to refute our belief.

ii. There are those who would argue that it is easier to find fault with something, i.e. an idea, belief, or hypothesis than to look for those things that would support it.

iii. It may be summed up in one word convention. It is conventional in social research to use null hypotheses. Null hypotheses, however, also perform specific functions in relation to probability theory and tests of research hypotheses.

iv. Under a probability theoretical model, hypotheses have a likelihood of being either true or false. Null hypotheses are particularly useful in such theoretical models. The null hypothesis is an expression of one alternative outcome of a social/physical observation.

The probability model specifies that the null hypotheses may be either true or false but not both simultaneously. Neither the research hypotheses nor the null hypothesis is absolutely true or absolutely false under any given test of it. Both probabilities (being either true or false) co-exist for each type of hypothesis always.

3. Scientific Hypotheses:

In scientific investigation, however, the term hypothesis is often given a somewhat more restricted meaning. To Braithwaite (1960) – A scientific hypothesis is a general proposition about all the things of a certain sort. It is an empirical proposition in the sense that it is testable by experience; experience is relevant to the question as to whether or not the hypothesis is true, i.e. as to whether or not it is a scientific law.’

A scientific hypothesis, in Braithwaite’s tradition, is a particular kind of proposition which, if true, will be accorded the status of a scientific law. The testability of a hypothesis is crucial, but there are many hypotheses within a theoretical system which cannot be directly tested against sense perception data.

Thus, ‘The empirical testing of the deductive system is effected by testing the lowest level hypotheses in the system. The confirmation or refutation of these is the criterion by which the truth of all the hypotheses in the system is tested’.

Since scientific hypothesis is often regarded as being a proposition where truth or falsity is capable of being asserted, the truth and falsity of it (hypothesis) can be determined only with respect to the domain of some theory.

4. Statistical Hypotheses:

These are statements about statistical population that, on the basis of information obtained from observed data, one seeks to support or refute. The statistical population may refer to either people or things. It is generally the case in the test of statistical hypotheses that observations about people or things are reduced in some way to numerical quantities, and decisions are made about these quantities.

To subject these hypotheses to empirical test, what is required is to reduce the variables used in them to measurable quantities. Research hypothesis and corresponding null hypotheses can be transferred into a statistical hypotheses that may be evaluated by numerical means.

Statistical hypotheses are usually established to delineate:

i. Differences between two or more groups regarding some trait or collection of characteristics that they possess,” association between two or more variables within one group or between several groups, and

ii. Point estimates of sample or population characteristics.

Levels of Hypothesis :

Apart from the aforesaid four types of hypotheses, three broad levels of hypotheses may be distinguished on the basis of the level of abstraction, which are as follows:

1. Some hypotheses state the existence of empirical uniformities. These hypotheses frequently, though not always, represent the scientific examination of common-sense propositions. They usually represent, also, a problem about which some ‘common-sense’ observation already exists. There are many types of such empirical uniformities which are common in social science and/or geographical research.

However, these investigations do not involve the testing of hypothesis at all, but are merely adding up the facts. These are not useful hypotheses for they merely represent what everyone already knows.

2. Some hypotheses are concerned with complex ideal types. These hypotheses aim at testing the existence of logically devised relationships between empirical uniformities. One such hypothesis was Ernest W. Burgess’s statement on the concentric growth circles that characterise the city.

This hypothesis was then tested against a variety of variables in a number of cities. That this ideal type does represent the actual patterns of city growth is not accepted by all ecologists, however, and so this formulation remains a hypothesis until a more crucial test of it is made.

Another hypothesis, concerning an ideal type also, results from these same ecological empirical uniformities. This was the notion that areas tend to represent certain characteristics in a series of predictable patterns. This was called the hypothesis of the ‘natural area’.

Much research has been done on this hypothesis, and the results, although they have modified the original statement somewhat, have generally supported it. With the growth of supporting evidence, notions about natural area have become a part of geographical theory rather than remaining hypotheses.

It is important to see that this level of hypothesising moves beyond the expectation of simple empirical uniformity, by creating a complex referent in society. The function of such hypothesis is to create tools and problems for further research in otherwise very complex area of investigation.

3. Some hypotheses are concerned with the relation of analytic variable. These hypotheses occur at a level of abstraction beyond that of ideal types. The hypotheses of empirical uniformities lead to the observation of simple differences, and those dealing with ideal types lead to specific coincidences of observations. The study of analytical variables requires the formulation of a relationship between changes in one property and changes in another.

On the basis of the above discussion, three major points can be identified:

(1) That a hypothesis is a necessary condition for successful research;

(2) That formulation of the hypothesis must be given considerable attention, to clarify its relation to theory, remove vague or value judgemental terms, and specify the test to be applied, and

(3) That hypotheses may be formulated on different levels of abstraction.

Functions of Hypotheses :

Theories are relatively elaborate tools used to explain and predict events. The social scientist develops a theory to account for some social phenomena, and then he devises a means whereby the theory can be tested or subjected to verification or refutation. Seldom does the researcher test theory directly. Most of the time he/she conducts tests of hypotheses that been generated and derived from that theory.

If the hypotheses ‘test out’ as the researcher has specified, or if his empirical observations are in accordance with what has been stated in the hypotheses, we say that his/her theory is supported in part. It usually takes many tests of different hypotheses from the same theory to demonstrate its predictive value and its adequacy as a tool of explanation for some event or sequence of events.

A major function of hypotheses is to make it possible to test theories. In this regard, an alternative definition of a hypothesis is that it is a statement of theory in testable forms. All statements of theory in testable form are called hypotheses.

Some hypotheses are not associated with any particular theory. It could be that as a result of some hypothesis, a theory will be eventually constructed. Consequently, another function of hypotheses is to suggest theories that may account far some event.

Although it is more often the case that research proceeds from theories to hypotheses, occasionally the reverse is true. The social investigator may have some idea about why a given phenomenon occurs and he/she hypothesises a number of things that relate to it.

He/she judges that some hypotheses have greater potential than others for explaining the event, and as a result, he/she may construct a logical system of propositions, assumptions and definitions linking his explanation to the events. In other words, the researcher devises a theory.

Working from the hypothesis back to the theory is not necessarily poor methodology. Eventually, the investigator is going to have to subject the resulting theory to empirical test to determine its adequacy. The predictive value of the theory can be assessed at that time.

Hypotheses also perform a descriptive function. Each time a hypothesis is tested empirically, that tells something about the phenomenon it is associated with. If the hypothesis is supported, then the information about the phenomenon increases.

Even if the hypothesis is refuted, the test tells something about the phenomenon that is not known before. The accumulation of information as a result of hypothesis testing reduces the amount of ignorance we may have about why a social event occurs in a given way.

Hypotheses also have some important secondary functions. As a result of testing certain hypotheses, social policy may be formulated in communities, penal institutions may be redesigned and revamped, teaching methods may be altered or improved solutions to various kinds of social problems may be suggested and implemented, and supervisory practices may be changed in factories and business.

Testing hypotheses refute certain ‘common sense’ notions about human behaviour, raises questions about explanations we presently use to account for things, and most generally alters our orientation towards our environment to one degree or another. All hypotheses have to do with our knowledge of things, and as this knowledge changes, we change also.

Testing Hypotheses :

Testing hypotheses means ‘subjecting them to some sort of empirical scrutiny to determine if they are supported or refuted by what the researcher observes’. Testing hypotheses means that the researcher will need to do a number of things.

Following are the two prerequisites to hypotheses testing:

1. A real social situation is needed that will suffice as a reasonable testing ground for the hypothesis. If the hypothesis concerns managerial behaviour, it will be necessary for the investigator to study some real organisation or organisations where managerial behaviour can be taken into empirically.

This particular prerequisite is frequently spoken of as ‘getting access to data that will enable the investigator to verify or refute his/her hypotheses’. Once a given social setting is selected, the relevant data in that situation must be obtained to make the hypothesis test a valid one.

2. The investigator should make sure that his hypotheses are testable. This means that he/she should limit his/her investigations to empirical phenomena or events that can be taken into through the senses. The variables used in the hypotheses tested should be amenable to measurement of some kind.

If they are not subject to measurement, the resulting test of the hypothesis will be relatively meaningless. Testing hypotheses must be a part of the empirical world. This is a fundamental requirement wherever the scientific method is employed in studying what is and why.

Terms that cannot be taken into empirically, render the hypothesis irrefutable and untestable. How can a scientist reject a hypothesis containing variables that he cannot experience in some empirical form? For example, if a researcher were to hypothesise that ‘evil spirit causes delinquency’, he/she can neither support nor refute this statement by using conventional scientific methods.

He/ she obviously has empirical tools to determine the incidence of delinquent or non-delinquent behaviour, but by what empirical means is he/she able to assess meaningfully the influence or impact of ‘evil spirits’ on delinquent behaviour?

Unless there are empirical means of evaluating the impact of non- empirical phenomena on particular variables, the researcher cannot validly subject the hypothesis to true scientific test. However, it is possible that terms that are presently indefinable empirically, might at some later date become amenable to the senses through the discovery of new means of measuring such phenomena. This always exists as a possibility.

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5 Characteristics of a Good Hypothesis: A Guide for Researchers

• by Brian Thomas
• October 10, 2023

Are you a curious soul, always seeking answers to the whys and hows of the world? As a researcher, formulating a hypothesis is a crucial first step towards unraveling the mysteries of your study. A well-crafted hypothesis not only guides your research but also lays the foundation for drawing valid conclusions. But what exactly makes a hypothesis a good one? In this blog post, we will explore the five key characteristics of a good hypothesis that every researcher should know.

Here, we will delve into the world of hypotheses, covering everything from their types in research to understanding if they can be proven true. Whether you’re a seasoned researcher or just starting out, this blog post will provide valuable insights on how to craft a sound hypothesis for your study. So let’s dive in and uncover the secrets to formulating a hypothesis that stands strong amidst the scientific rigor!

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5 Characteristics of a Good Hypothesis

Clear and specific.

A good hypothesis is like a GPS that guides you to the right destination. It needs to be clear and specific so that you know exactly what you’re testing. Avoid vague statements or general ideas. Instead, focus on crafting a hypothesis that clearly states the relationship between variables and the expected outcome. Clarity is key, my friend!

Testable and Falsifiable

A hypothesis might sound great in theory, but if you can’t test it or prove it wrong, then it’s like chasing unicorns. A good hypothesis should be testable and falsifiable – meaning there should be a way to gather evidence to support or refute it. Don’t be afraid to challenge your hypothesis and put it to the test. Only when it can be proven false can it truly be considered a good hypothesis.

Based on Existing Knowledge

Imagine trying to build a Lego tower without any Lego bricks. That’s what it’s like to come up with a hypothesis that has no basis in existing knowledge. A good hypothesis is grounded in previous research, theories, or observations. It shows that you’ve done your homework and understand the current state of knowledge in your field. So, put on your research hat and gather those building blocks for a solid hypothesis!

Specific Predictions

No, we’re not talking about crystal ball predictions or psychic abilities here. A good hypothesis includes specific predictions about what you expect to happen. It’s like making an educated guess based on your understanding of the variables involved. These predictions help guide your research and give you something concrete to look for. So, put on those prediction goggles, my friend, and let’s get specific!

Relevant to the Research Question

A hypothesis is a road sign that points you in the right direction. But if it’s not relevant to your research question, then you might end up in a never-ending detour. A good hypothesis aligns with your research question and addresses the specific problem or phenomenon you’re investigating. Keep your focus on the main topic and avoid getting sidetracked by shiny distractions. Stay relevant, my friend, and you’ll find the answers you seek!

And there you have it: the five characteristics of a good hypothesis. Remember, a good hypothesis is clear, testable, based on existing knowledge, makes specific predictions, and is relevant to your research question. So go forth, my friend, and hypothesize your way to scientific discovery!

FAQs: Characteristics of a Good Hypothesis

In the realm of scientific research, a hypothesis plays a crucial role in formulating and testing ideas. A good hypothesis serves as the foundation for an experiment or study, guiding the researcher towards meaningful results. In this FAQ-style subsection, we’ll explore the characteristics of a good hypothesis, their types, formulation, and more. So let’s dive in and unravel the mysteries of hypothesis-making!

What Are Two Important Characteristics of a Good Hypothesis

A good hypothesis possesses two important characteristics:

Testability : A hypothesis must be testable to determine its validity. It should be formulated in a way that allows researchers to design and conduct experiments or gather data for analysis. For example, if we hypothesize that “drinking herbal tea reduces stress,” we can easily test it by conducting a study with a control group and a group drinking herbal tea.

Falsifiability : Falsifiability refers to the potential for a hypothesis to be proven wrong. A good hypothesis should make specific predictions that can be refuted or supported by evidence. This characteristic ensures that hypotheses are based on empirical observations rather than personal opinions. For instance, the hypothesis “all swans are white” can be falsified by discovering a single black swan.

What Are the Types of Hypothesis in Research

In research, there are three main types of hypotheses:

Null Hypothesis (H0) : The null hypothesis is a statement of no effect or relationship. It assumes that there is no significant difference between variables or no effect of a treatment. Researchers aim to reject the null hypothesis in favor of an alternative hypothesis.

Alternative Hypothesis (HA or H1) : The alternative hypothesis is the opposite of the null hypothesis. It asserts that there is a significant difference between variables or an effect of a treatment. Researchers seek evidence to support the alternative hypothesis.

Directional Hypothesis : A directional hypothesis predicts the specific direction of the relationship or difference between variables. For example, “increasing exercise duration will lead to greater weight loss.”

Can a Hypothesis Be Proven True

In scientific research, hypotheses are not proven true; they are supported or rejected based on empirical evidence . Even if a hypothesis is supported by multiple studies, new evidence could arise that contradicts it. Scientific knowledge is always subject to revision and refinement. Therefore, the goal is to gather enough evidence to either support or reject a hypothesis, rather than proving it absolutely true.

What Are the Six Parts of a Hypothesis

A hypothesis typically consists of six essential parts:

Research Question : A clear and concise question that the hypothesis seeks to answer.

Variables : Identification of the independent (manipulated) and dependent (measured) variables involved in the hypothesis.

Population : The specific group or individuals the hypothesis is concerned with.

Relationship or Comparison : The expected relationship or difference between variables, often indicated by directional terms like “more,” “less,” “higher,” or “lower.”

Predictability : A statement of the predicted outcome or result based on the relationship between variables.

Testability : The ability to design an experiment or gather data to support or reject the hypothesis.

How Do You Start a Hypothesis Sentence

When starting a hypothesis sentence, it is essential to use clear and concise language to express your ideas. A common approach is to use the phrase “If…then…” to establish the conditional relationship between variables. For example:

• If [independent variable], then [dependent variable] because [explanation of expected relationship].

This structure allows for a straightforward and logical formulation of the hypothesis.

What Are Examples of Hypotheses

Here are a few examples of well-formulated hypotheses:

If exposure to sunlight increases, then plants will grow taller because sunlight is necessary for photosynthesis.

If students receive praise for good grades, then their motivation to excel will increase because they seek recognition and approval.

If the dose of a painkiller is increased, then the relief from pain will last longer because a higher dosage has a prolonged effect.

What Are the Five Key Elements to a Good Hypothesis

A good hypothesis should include the following five key elements:

Clarity : The hypothesis should be clear and specific, leaving no room for interpretation.

Testability : It should be possible to test the hypothesis through experimentation or data collection.

Relevance : The hypothesis should be directly tied to the research question or problem being investigated.

Specificity : It must clearly state the relationship or difference between variables being studied.

Falsifiability : The hypothesis should make predictions that can be refuted or supported by empirical evidence.

What Makes a Good Hypothesis in a Research Paper

In a research paper, a good hypothesis should have the following characteristics:

Relevance : It must directly relate to the research topic and address the objectives of the study.

Clarity : The hypothesis should be concise and precisely worded to avoid confusion.

Unambiguous : It must leave no room for multiple interpretations or ambiguity.

Logic : The hypothesis should be based on rational and logical reasoning, considering existing theories and observations.

Empirical Support : Ideally, the hypothesis should be supported by prior empirical evidence or strong theoretical justifications.

Is a Hypothesis Always a Question

No, a hypothesis is not always in the form of a question. While some hypotheses can take the form of a question, others may be statements asserting a relationship or difference between variables. The form of a hypothesis depends on the research question being addressed and the researcher’s preferred style of expression.

What Are the Three Things Needed for a Good Hypothesis

For a hypothesis to be considered good, it must fulfill the following three criteria:

Testability : The hypothesis should be formulated in a way that allows for empirical testing through experimentation or data collection.

Falsifiability : It must make specific predictions that can be potentially refuted or supported by evidence.

Relevance : The hypothesis should directly address the research question or problem being investigated.

What Are the Four Components to a Good Hypothesis

A good hypothesis typically consists of four components:

Independent Variable : The variable being manipulated or controlled by the researcher.

Dependent Variable : The variable being measured or observed to determine the effect of the independent variable.

Directionality : The predicted relationship or difference between the independent and dependent variables.

Population : The specific group or individuals to which the hypothesis applies.

How Do You Formulate a Hypothesis

To formulate a hypothesis, follow these steps:

Identify the Research Topic : Clearly define the area or phenomenon you want to study.

Conduct Background Research : Review existing literature and research to gain knowledge about the topic.

Formulate a Research Question : Ask a clear and focused question that you want to answer through your hypothesis.

State the Null and Alternative Hypotheses : Develop a null hypothesis to assume no effect or relationship, and an alternative hypothesis to propose a significant effect or relationship.

Decide on Variables and Relationships : Determine the independent and dependent variables and the predicted relationship between them.

Refine and Test : Refine your hypothesis, ensuring it is clear, testable, and falsifiable. Then, design experiments or gather data to support or reject it.

What Is a Characteristic of a Hypothesis MCQ

Multiple-choice questions (MCQ) regarding the characteristics of a hypothesis often assess knowledge on the testability and falsifiability of hypotheses. They may ask about the criteria that distinguish a good hypothesis from a poor one or the importance of making specific predictions. Remember to choose answers that emphasize the empirical and testable nature of hypotheses.

What Five Criteria Must Be Satisfied for a Hypothesis to Be Scientific

For a hypothesis to be considered scientific, it must satisfy the following five criteria:

Testability : The hypothesis must be formulated in a way that allows it to be tested through experimentation or data collection.

Falsifiability : It should make specific predictions that can be potentially refuted or supported by empirical evidence.

Empirical Basis : The hypothesis should be based on empirical observations or existing theories and knowledge.

Relevance : It must directly address the research question or problem being investigated.

Objective : A scientific hypothesis should be free from personal biases or subjective opinions, focusing on objective observations and analysis.

What Are the Steps of Theory Development in Scientific Methods

In scientific methods, theory development typically involves the following steps:

Observation : Identifying a phenomenon or pattern worthy of investigation through observation or empirical data.

Formulation of a Hypothesis : Constructing a hypothesis that explains the observed phenomena or predicts a relationship between variables.

Data Collection : Gathering relevant data through experiments, surveys, observations, or other research methods.

Analysis : Analyzing the collected data to evaluate the hypothesis’s predictions and determine their validity.

Revision and Refinement : Based on the analysis, refining the hypothesis, modifying the theory, or formulating new hypotheses for further investigation.

Which of the Following Makes a Good Hypothesis

A good hypothesis is characterized by:

Testability : The ability to form experiments or gather data to support or refute the hypothesis.

Falsifiability : The potential for the hypothesis’s predictions to be proven wrong based on empirical evidence.

Clarity : A clear and concise statement or question that leaves no room for ambiguity.

Relevancy : Directly addressing the research question or problem at hand.

Remember, it is important to select the option that encompasses all these characteristics.

What Are the Characteristics of a Good Hypothesis

A good hypothesis possesses several characteristics, such as:

Testability : It should allow for empirical testing through experiments or data collection.

Falsifiability : The hypothesis should make specific predictions that can be potentially refuted or supported by evidence.

Clarity : It must be clearly and precisely formulated, leaving no room for ambiguity or multiple interpretations.

Relevance : The hypothesis should directly relate to the research question or problem being investigated.

What Is the Five-Step p-value Approach to Hypothesis Testing

The five-step p-value approach is a commonly used framework for hypothesis testing:

Step 1: Formulating the Hypotheses : The null hypothesis (H0) assumes no effect or relationship, while the alternative hypothesis (HA) proposes a significant effect or relationship.

Step 2: Setting the Significance Level : Decide on the level of significance (α), which represents the probability of rejecting the null hypothesis when it is true. The commonly used level is 0.05 (5%).

Step 3: Collecting Data and Performing the Test : Acquire and analyze the data, calculating the test statistic and the corresponding p-value.

Step 4: Comparing the p-value with the Significance Level : If the p-value is less than the significance level (α), reject the null hypothesis. Otherwise, fail to reject the null hypothesis.

Step 5: Drawing Conclusions : Based on the comparison in Step 4, interpret the results and draw conclusions about the hypothesis.

What Are the Stages of Hypothesis

The stages of hypothesis generally include:

Observation : Identifying a pattern, phenomenon, or research question that warrants investigation.

Formulation : Developing a hypothesis that explains or predicts the relationship or difference between variables.

Testing : Collecting data, designing experiments, or conducting studies to gather evidence supporting or refuting the hypothesis.

Analysis : Assessing the collected data to determine whether the results support or reject the hypothesis.

Conclusion : Drawing conclusions based on the analysis and making further iterations, refinements, or new hypotheses for future research.

What Is a Characteristic of a Good Hypothesis

A characteristic of a good hypothesis is its ability to make specific predictions about the relationship or difference between variables. Good hypotheses avoid vague statements and clearly articulate the expected outcomes. By doing so, researchers can design experiments or gather data that directly test the predictions, leading to meaningful results.

How Do You Write a Good Hypothesis Example

To write a good hypothesis example, follow these guidelines:

If possible, use the “If…then…” format to express a conditional relationship between variables.

Be clear and concise in stating the variables involved, the predicted relationship, and the expected outcome.

Ensure the hypothesis is testable, meaning it can be evaluated through experiments or data collection.

For instance, consider the following example:

If students study for longer periods of time, then their test scores will improve because increased study time allows for better retention of information and increased proficiency.

What Is the Difference Between Hypothesis and Hypotheses

The main difference between a hypothesis and hypotheses lies in their grammatical number. A hypothesis refers to a single statement or proposition that is formulated to explain or predict the relationship between variables. On the other hand, hypotheses is the plural form of the term hypothesis, commonly used when multiple statements or propositions are proposed and tested simultaneously.

What Is a Good Hypothesis Statement

A good hypothesis statement exhibits the following qualities:

Clarity : It is written in clear and concise language, leaving no room for confusion or ambiguity.

Testability : The hypothesis should be formulated in a way that enables testing through experiments or data collection.

Specificity : It must clearly state the predicted relationship or difference between variables.

By adhering to these criteria, a good hypothesis statement guides research efforts effectively.

What Is Not a Characteristic of a Good Hypothesis

A characteristic that does not align with a good hypothesis is subjectivity . A hypothesis should be objective, based on empirical observations or existing theories, and free from personal bias. While personal interpretations and opinions can inspire the formulation of a hypothesis, it must ultimately rely on objective observations and be open to empirical testing.

By now, you’ve gained insights into the characteristics of a good hypothesis, including testability, falsifiability, clarity,

• characteristics
• falsifiable
• good hypothesis
• hypothesis testing
• null hypothesis
• observations
• scientific rigor

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• 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 .

Prevent plagiarism. Run a free check.

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

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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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What Are the Elements of a Good Hypothesis?

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A hypothesis is an educated guess or prediction of what will happen. In science, a hypothesis proposes a relationship between factors called variables. A good hypothesis relates an independent variable and a dependent variable. The effect on the dependent variable depends on or is determined by what happens when you change the independent variable . While you could consider any prediction of an outcome to be a type of hypothesis, a good hypothesis is one you can test using the scientific method. In other words, you want to propose a hypothesis to use as the basis for an experiment.

Cause and Effect or 'If, Then' Relationships

A good experimental hypothesis can be written as an if, then statement to establish cause and effect on the variables. If you make a change to the independent variable, then the dependent variable will respond. Here's an example of a hypothesis:

If you increase the duration of light, (then) corn plants will grow more each day.

The hypothesis establishes two variables, length of light exposure, and the rate of plant growth. An experiment could be designed to test whether the rate of growth depends on the duration of light. The duration of light is the independent variable, which you can control in an experiment . The rate of plant growth is the dependent variable, which you can measure and record as data in an experiment.

Key Points of Hypothesis

When you have an idea for a hypothesis, it may help to write it out in several different ways. Review your choices and select a hypothesis that accurately describes what you are testing.

• Does the hypothesis relate an independent and dependent variable? Can you identify the variables?
• Can you test the hypothesis? In other words, could you design an experiment that would allow you to establish or disprove a relationship between the variables?
• Would your experiment be safe and ethical?
• Is there a simpler or more precise way to state the hypothesis? If so, rewrite it.

What If the Hypothesis Is Incorrect?

It's not wrong or bad if the hypothesis is not supported or is incorrect. Actually, this outcome may tell you more about a relationship between the variables than if the hypothesis is supported. You may intentionally write your hypothesis as a null hypothesis or no-difference hypothesis to establish a relationship between the variables.

For example, the hypothesis:

The rate of corn plant growth does not depend on the duration of light.

This can be tested by exposing corn plants to different length "days" and measuring the rate of plant growth. A statistical test can be applied to measure how well the data support the hypothesis. If the hypothesis is not supported, then you have evidence of a relationship between the variables. It's easier to establish cause and effect by testing whether "no effect" is found. Alternatively, if the null hypothesis is supported, then you have shown the variables are not related. Either way, your experiment is a success.

Need more examples of how to write a hypothesis ? Here you go:

• If you turn out all the lights, you will fall asleep faster. (Think: How would you test it?)
• If you drop different objects, they will fall at the same rate.
• If you eat only fast food, then you will gain weight.
• If you use cruise control, then your car will get better gas mileage.
• If you apply a top coat, then your manicure will last longer.
• If you turn the lights on and off rapidly, then the bulb will burn out faster.
• What Are Examples of a Hypothesis?
• What Is a Testable Hypothesis?
• What Is a Hypothesis? (Science)
• Scientific Hypothesis Examples
• Six Steps of the Scientific Method
• Scientific Method Flow Chart
• Null Hypothesis Examples
• Understanding Simple vs Controlled Experiments
• Scientific Method Vocabulary Terms
• What Is a Controlled Experiment?
• What Is an Experimental Constant?
• Scientific Variable
• What Is the Difference Between a Control Variable and Control Group?
• DRY MIX Experiment Variables Acronym
• Random Error vs. Systematic Error
• The Role of a Controlled Variable in an Experiment

Physical Geography

Scientific method.

You have probably learned that the  scientific method  is a series of steps that help to investigate. To answer those questions, scientists use data and evidence gathered from observations, experience, or experiments to answer their questions.But scientific inquiry rarely proceeds in the same sequence of steps outlined by the scientific method. For example, the order of the steps might change because more questions arise from the data that is collected. Still, to come to verifiable conclusions, logical, repeatable steps of the scientific method must be followed. This video of The Scientific Method Made Easy explains scientific method succinctly and well.

Scientific Questioning

The most important thing a scientist can do is to ask questions.

• What makes Mount St. Helens more explosive and dangerous than the volcano on Mauna Loa, Hawaii?
• What makes the San Andreas fault different than the Wasatch Fault?
• Why does Earth have so many varied life forms but other planets in the solar system do not?
• What impacts could a warmer planet have on weather and climate systems?

Earth science can answer testable questions about the natural world. What makes a question impossible to test? Some untestable questions are whether ghosts exist or whether there is life after death. A testable question might be about how to reduce soil erosion on a farm. A farmer has heard of a planting method called “no-till farming.” Using this process eliminates the need for plowing the land. The farmer’s question is: Will no-till farming reduce the erosion of the farmland?

Scientific Research

To answer a question, a scientist first finds out what is already known about the topic by reading books and magazines, searching the Internet, and talking to experts. This information will allow the scientist to create a good experimental design. If this question has already been answered, the research may be enough or it may lead to new questions.

The farmer researches no-till farming on the Internet, at the library, at the local farming supply store, and elsewhere. He learns about various farming methods; he learns what type of fertilizer is best to use and what the best crop spacing would be. From his research he learns that no-till farming can be a way to reduce carbon dioxide emissions into the atmosphere, which helps in the fight against global warming.

With the information collected from background research, the scientist creates a plausible explanation for the question. This is a hypothesis. The hypothesis must directly relate to the question and must be testable. Having a hypothesis guides a scientist in designing experiments and interpreting data.

The farmer’s hypothesis is this: No-till farming will decrease soil erosion on hills of similar steepness as compared to the traditional farming technique because there will be fewer disturbances to the soil.

Data Collection

To support or refute a hypothesis, the scientist must collect data. A great deal of logic and effort goes into designing tests to collect data so the data can answer scientific questions. Data is usually collected by experiment or observation. Sometimes improvements in technology will allow new tests to better address a hypothesis.

Observation is used to collect data when it is not possible for practical or ethical reasons to perform experiments. Written descriptions are qualitative data based on observations. This data may also be used to answer questions. Scientists use many different types of instruments to make quantitative measurements. Electron microscopes can be used to explore tiny objects or telescopes to learn about the universe. Probes make observations where it is too dangerous or too impractical for scientists to go. Data from the probes travels through cables or through space to a computer where it is manipulated by scientists.

Experiments may involve chemicals and test tubes, or they may require advanced technologies like a high-powered electron microscope or radio telescope. Atmospheric scientists may collect data by analyzing the gases present in gas samples, and geochemists may perform chemical analyses on rock samples.

A good experiment must have one factor that can be manipulated or changed. This is the independent variable. The rest of the factors must remain the same. They are the experimental controls. The outcome of the experiment, or what changes as a result of the experiment, is the dependent variable. The dependent variable “depends” on the independent variable.

The farmer conducts an experiment on two separate hills. The hills have similar steepness and receive similar amounts of sunshine. On one, the farmer uses a traditional farming technique that includes plowing. On the other, he uses a no-till technique, spacing plants farther apart and using specialized equipment for planting. The plants on both hillsides receive identical amounts of water and fertilizer. The farmer measures plant growth on both hillsides. In this experiment:

• What is the independent variable?
• What are the experimental controls?
• What is the dependent variable?

The independent variable is the farming technique—either traditional or no-till—because that is what is being manipulated. For a fair comparison of the two farming techniques, the two hills must have the same slope and the same amount of fertilizer and water. These are the experimental controls. The amount of erosion is the dependent variable. It is what the farmer is measuring.

During an experiment, scientists make many measurements. Data in the form of numbers is quantitative.

Data gathered from advanced equipment usually goes directly into a computer, or the scientist may put the data into a spreadsheet. The data then can be manipulated. Charts and tables display data and should be clearly labeled. Statistical analysis makes more effective use of data by allowing scientists to show relationships between different categories of data. Statistics can make sense of the variability in a data set. Graphs help scientists to visually understand the relationships between data. Pictures are created so that other people who are interested can see the relationships easily.

In just about every human endeavor, errors are unavoidable. In a scientific experiment, this is called experimental error. What are the sources of experimental errors? Systematic errors may be inherent in the experimental setup so that the numbers are always skewed in one direction. For example, a scale may always measure one-half ounce high. The error will disappear if the scale is re-calibrated. Random errors occur because a measurement is not made precisely. For example, a stopwatch may be stopped too soon or too late. To correct for this type of error, many measurements are taken and then averaged. If a result is inconsistent with the results from other samples and many tests have been done, it is likely that a mistake was made in that experiment and the inconsistent data point can be thrown out.

Conclusions

Scientists study graphs, tables, diagrams, images, descriptions, and all other available data to draw a conclusion from their experiments. Is there an answer to the question based on the results of the experiment? Was the hypothesis supported? Some experiments completely support a hypothesis and some do not. If a hypothesis is shown to be wrong, the experiment was not a failure. All experimental results contribute to knowledge. Experiments that do or do not support a hypothesis may lead to even more questions and more experiments.

After a year, the farmer finds that erosion on the traditionally farmed hill is 2.2 times greater than erosion on the no-till hill. The plants on the no-till plots are taller and the soil moisture is higher. The farmer decides to convert to no-till farming for future crops. The farmer continues researching to see what other factors may help reduce erosion.

As scientists conduct experiments and make observations to test a hypothesis, over time they collect a lot of data. If a hypothesis explains all the data and none of the data contradicts the hypothesis, the hypothesis becomes a theory. A scientific theory is supported by many observations and has no major inconsistencies. A theory must be constantly tested and revised. Once a theory has been developed, it can be used to predict behavior. A theory provides a model of reality that is simpler than the phenomenon itself. Even a theory can be overthrown if conflicting data is discovered. However, a longstanding theory that has lots of evidence to back it up is less likely to be overthrown than a newer theory.

• Dynamic Earth: Introduction to Physical Geography. Authored by : R. Adam Dastrup. Located at : http://www.opengeography.org/physical-geography.html . Project : Open Geography Education. License : CC BY-SA: Attribution-ShareAlike
• 10 - The Scientific Method Made Easy. Authored by : potholer54. Located at : https://youtu.be/zcavPAFiG14 . License : All Rights Reserved . License Terms : Standard YouTube License
• Know the Difference (Between Hypothesis and Theory). Authored by : OSUbiology. Located at : https://youtu.be/jdWMcMW54fA . License : All Rights Reserved . License Terms : Standard YouTube License

Aims & Hypothesis ( CIE IGCSE Geography )

Revision note.

Geography Lead

Aims & Hypothesis

Aims/hypothesis.

• This is linked to the content in the specification and then related to a place-specific context
• All fieldwork begins with the aims and hypothesis
• An investigation into changes in beach profiles along Mappleton Beach
• An investigation into the impact of building a wind farm in rural Lincolnshire
• River discharge increases with distance from the source of the River Dove
• Environmental quality increases with distance from the new housing estate in  Swanland, East Yorkshire
• Aims and hypothesis may be based on what is already known about the topic. For example, Bradshaw's model in rivers

When answering Hypotheses questions that ask whether you agree or not, always give your opinion at the start of your answer before any supporting evidence. This will usually be Yes, No or Partially True /True to some extent.

Do not just copy out the Hypothesis if you agree with it. It is important to make a decision and state it as well as provide the evidence for your choice. Be clear in your decision –expressions such as ‘might be true’, ‘could be false’, ‘true and false’ are too vague.

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Author: Bridgette

After graduating with a degree in Geography, Bridgette completed a PGCE over 25 years ago. She later gained an MA Learning, Technology and Education from the University of Nottingham focussing on online learning. At a time when the study of geography has never been more important, Bridgette is passionate about creating content which supports students in achieving their potential in geography and builds their confidence.

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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."

75+ A-Level Geography Investigation NEA Ideas

In A-Level by Think Student Editor January 5, 2021 Leave a Comment

A-Level coursework is one of the most interesting and stressful parts of an A-Level course: you finally get to have control over what you research and write about…but where do you start? An NEA or Independent Investigation is completely your own research report. Besides all the formatting questions, the first challenge you have to overcome is figuring out what question to research. Where do you begin? How do you filter out all your ideas into just one question?

Don’t worry if you don’t have an idea for your NEA straight away – I definitely didn’t! This article is here to give you some inspiration for the subject for your A-Level Geography NEA.

What is an A-Level Geography NEA?

A-Level Geography NEA is the coursework part of Geography A-Level . It’s a compulsory part of the A-Level, meaning that it’s graded against a set of assessment objectives, like your exams. For your NEA coursework, you choose your own question based on either physical or human geography .

Physical geography NEA ideas could relate to the coast, glaciers, or other areas of the environment. Typically, NEA questions will relate to a topic you’ve studied as part of the exam portion of your A-Level, but they don’t have to be.

Human geography NEA ideas could relate to urban areas, migration, or globalisation and other human development areas. Most of the time, they’ll be focused on your local area , but it doesn’t have to be if you feel like you’d be too restricted!

Linked here are the guidelines for A-Level Geography NEA for all four UK exam boards: AQA , OCR , Pearson Edexcel , and WJEC Eduqas .

What does an A-Level Geography NEA look like?

The exam boards, and your school, allow you to compile your research findings however you like . You can include graphs, charts, photos, in any colour or order you want.

However, there will probably be some rough guidelines, as an NEA is expected to be formatted like a typical research paper . Graphs, charts, and photos should be labelled, and you should use a sensible font and letter size.

The Royal Geographical Society has a student guide to completing an NEA, linked here .

How long is an A-Level Geography NEA?

Generally, there is no official cap on the word count for an NEA report, but most exam boards and schools suggest 3000-4000 words. 2000 words is generally thought to be too little, and anything above 6,000 words is considered too much.

The reason for an official cap on an NEA word count is that everyone’s research is unique, and you’ll have different things to say than other people, which may take more or less words.

My A-Level Geography NEA was 5,000 words, so even though it was a little over the recommended word limit, it was still allowed. When writing your NEA, try and keep your sentences clear and concise.

How much of A-Level Geography is an NEA worth?

For the four UK exam boards, an A-Level Geography NEA is worth 20% of your A-Level. This may not seem like a lot relative to the amount of work you’ll put into an NEA, but it’s worth more than you might think. It essentially replaces one exam.

It’s also worth it for the experience, as many of the formatting and writing techniques you pick up during an NEA can be taken into higher education.

This Think Student article has a list of the most respected A-Level subjects, of which A-Level Geography is a part!

What makes a good A-Level Geography NEA idea?

An NEA is a very individual experience, and what makes an idea “good” is also quite personal . It’s a good idea to pick a question based on something you’re actually interested in, because your enthusiasm will show through your work and boost your investigation that much more.

“Good” NEA ideas are usually the ones that allow you to use several different data sets . This means you aren’t just collecting one type of data, and you have to use different data presentations and a wide range of analysis.

Similarly, your NEA idea should be broad enough to explore a few different ideas, but also be focused in one particular area , e.g. coastal management. NEA ideas are usually either a topic from physical geography or human geography, but it can sometimes be a mix of both.

A-Level Geography NEA ideas

Below is a list of ideas for your A-Level Geography NEA, split into physical and human geography. These are just ideas to get you thinking about what you want to write about – your NEA title needs to be formatted as a question.

I hope these ideas have given you inspiration. Good luck writing your NEA!

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What makes a good hypothesis.

A carefully chosen and well-written hypothesis makes it easy to design an experiment to test and learn from. So here are six steps to follow when crafting your prediction.

A hypothesis is essentially a statement of belief that expresses why you think your innovation, or change to your product or service, will create value. The hypothesis is what you test when you run experiments, to try and turn that belief into more certain knowledge.

For example, Amazon was created on the belief that people would be happy to buy books online. Similarly, smartphones were created on the belief that customers would be willing to pay a premium for a phone that offered additional functionality other than calls and texts.

Every experiment you run should be designed to test a particular hypothesis, and its usefulness will depend in part on how well you have articulated that hypothesis.

So how do you create and articulate a good hypothesis?

1. Break it down

You are unlikely to be able to test your entire innovation with one hypothesis. Even in the early stages of testing the fundamental idea behind your product or service, there are a few different assumptions that need exploring. In his book ' The Lean Startup ', Eric Ries identifies two in particular: the value hypothesis and the growth hypothesis.

The value hypothesis is designed to test whether your product or service provides potential customers with enough value once they are using it (and therefore, whether they would be willing to pay for it). Creating a Concierge MVP , for example, allows you to test the hypothesis that customers are willing to pay for a particular service before you build the automated version.

The growth hypothesis tests how new customers will find and start using your new product or service. If you have assumed that you can grow your customer base through word-of-mouth referrals, for example, testing how many users of your MVP go on to tell their friends about it will give you an idea of whether that is likely to work.

As you continue developing your idea, you might want to test smaller, less fundamental assumptions. For example, if you are building a digital product, you might want to test whether a 'Buy Now' button works better at the top or the bottom of a particular page. Each of these tests rests on a single hypothesis, and articulating that hypothesis clearly can help you design better experiments.

2. Start with what you know

A great way to start building your hypothesis is with the phrase 'We believe...' This allows you to express clearly the assumption on which you are building your hypothesis, and ensure that it is tied to what you think you know about your customers .

"Many people are unable to clearly articulate their current understanding and assumptions about their business and their customer. If you are unable to clearly articulate these and you are trying to run experiments, it's a red flag. It's probably better to start with developing a better understanding of your customer than running haphazard experiments." James Birchler

3. Test one thing at a time

Don't be tempted to create a hypothesis that covers more than one aspect of your innovation. While it may feel like it saves time, in actual fact you'll be unable to distinguish which aspect has caused the results of your experiment. For example, changing two aspects of a sign-up page and then testing won't tell you which of those aspects caused any resulting change in the number of sign-ups.

4. Make it measurable

How will you know if your hypothesis is correct? Before you can experiment, you need to set a measure that is right for your innovation project. There are two aspects to making your hypothesis measurable: choosing the right metric and setting a clear objective.

The metric you choose should be as closely tied to what you are testing as possible: for example, if you are testing the value hypothesis, asking potential customers if they would pay for a particular product or service is not as reliable as building a Concierge MVP and seeing if they actually do. Think hard about what metric will give you the most useful information.

Similarly, be precise about your objective. Testing whether customers will pay for a particular product or service is one thing – but finding out if enough customers will pay for it allows you to be much more certain that it is worth the cost of developing it.

5. Set a timeframe

Imagine that you've decided that you need 20 customers to use your Concierge MVP and pay for the service to prove your hypothesis. You get your 20 customers, but over the course of two months. Is 10 customers a month enough to prove your hypothesis? What about ten customers a year? Each of these represents a different level of interest and will be crucial in forecasting potential revenues for your product or service. So be clear about your timeframe – how long will you run the experiment in order to generate useful results, and what do you expect to happen in that timeframe?

6. Express it clearly and simply – and don't go overboard

There are many different ways of structuring your hypotheses, and you need to find the one that works for you and your organization. We've recommended starting your hypothesis with the phrase 'We believe...' in order to articulate your assumptions clearly, and one approach is to follow this up with 'We will test this by...' and 'We are right if...' These simple phrases encourage you to include the basic elements of a good hypothesis without overcomplicating matters – for example:

'We believe that customers will be willing to buy books online. We will test this by setting up a Concierge MVP where customers can order books. When customers order, we will then go to the bookshop, buy the books, and send them to the customer. We are right if x customers per month over six months order a book from us.'

Further reading:

• The Lean Startup – Eric Ries
• The Real Startup Book Hypothesis Checklist
• Lean Startup Best Practices – James Birchler
• How to make good lean startup hypotheses – Tim Kastelle

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19 Facts About Tim Walz, Harris’s Pick for Vice President

Mr. Walz, the governor of Minnesota, worked as a high school social studies teacher and football coach, served in the Army National Guard and chooses Diet Mountain Dew over alcohol.

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By Simon J. Levien and Maggie Astor

• Published Aug. 6, 2024 Updated Aug. 9, 2024

Until recently, Gov. Tim Walz of Minnesota was a virtual unknown outside of the Midwest, even among Democrats. But his stock rose fast in the days after President Biden withdrew from the race, clearing a path for Ms. Harris to replace him and pick Mr. Walz as her No. 2.

Here’s a closer look at the Democrats’ new choice for vice president.

1. He is a (very recent) social media darling . Mr. Walz has enjoyed a groundswell of support online from users commenting on his Midwestern “dad vibes” and appealing ordinariness.

2. He started the whole “weird” thing. It was Mr. Walz who labeled former President Donald J. Trump and his running mate, Senator JD Vance of Ohio, “weird” on cable television just a couple of weeks ago. The description soon became a Democratic talking point.

3. He named a highway after Prince and signed the bill in purple ink. “I think we can lay to rest that this is the coolest bill signing we’ll ever do,” he said as he put his name on legislation declaring a stretch of Highway 5 the “Prince Rogers Nelson Memorial Highway” after the musician who had lived in Minnesota.

4. He reminds you of your high school history teacher for a reason. Mr. Walz taught high school social studies and geography — first in Alliance, Neb., and then in Mankato, Minn. — before entering politics.

5. He taught in China in 1989 and speaks some Mandarin. He went to China for a year after graduating from college and taught English there through a program affiliated with Harvard University.

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