hypothesis is a speculative statement about the relationship

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Hypothesis | Definition

Hypothesis refers to a testable statement or prediction about the relationship between two or more variables in scientific research.

Understanding Hypothesis

In social science research, a hypothesis plays a crucial role in guiding the research process. It is essentially an educated guess or a prediction that researchers formulate based on existing theories, observations, or knowledge. A hypothesis helps define the direction of the study and provides a framework for data collection and analysis.

The Importance of a Hypothesis

A hypothesis is central to the research process for several reasons:

• Focuses the Study : By making a specific prediction, the hypothesis narrows the focus of the research. Instead of exploring a broad question, researchers can zero in on testing the specific prediction made by the hypothesis.
• Guides Research Design : Once a hypothesis is formulated, researchers can design their study in a way that either supports or refutes the hypothesis. This includes choosing appropriate research methods, collecting relevant data, and conducting analyses.
• Provides Direction : A clear hypothesis helps ensure that the research is purposeful and organized. It gives researchers a goal to work toward and a means to measure their findings against their predictions.
• Enables Testing of Theories : Many hypotheses are derived from existing theories. By testing a hypothesis, researchers can assess whether the theory holds up in different contexts or under different conditions.

Components of a Hypothesis

A well-formulated hypothesis usually contains several key components:

• Variables : These are the elements that the researcher is studying. Typically, a hypothesis involves an independent variable (the cause or predictor) and a dependent variable (the effect or outcome). For example, a researcher might hypothesize that “increased study time (independent variable) leads to higher test scores (dependent variable).”
• Relationship : The hypothesis also specifies the expected relationship between the variables. In the example above, the hypothesis predicts a positive relationship between study time and test scores.
• Testability : A hypothesis must be testable through empirical observation or experimentation. If a hypothesis cannot be tested, it remains a speculation or an idea rather than a scientific hypothesis.
• Falsifiability : For a hypothesis to be scientific, it must be falsifiable, meaning that it can be proven wrong. If a hypothesis cannot be disproven, it is not considered scientifically valid.

Types of Hypotheses

There are several types of hypotheses used in social science research, each serving a unique purpose. The most common types are:

1. Null Hypothesis (H0)

The null hypothesis asserts that there is no relationship between the variables being studied. It acts as a default assumption that the researcher tries to disprove or reject. For example, the null hypothesis might state, “There is no relationship between study time and test scores.”

Researchers typically use statistical tests to determine whether they can reject the null hypothesis. If the evidence suggests a significant relationship between the variables, the null hypothesis is rejected.

2. Alternative Hypothesis (H1)

The alternative hypothesis suggests that there is a relationship between the variables. It is the opposite of the null hypothesis. For example, the alternative hypothesis might state, “Increased study time is associated with higher test scores.”

The goal of the research is usually to provide enough evidence to support the alternative hypothesis.

3. Directional Hypothesis

A directional hypothesis makes a specific prediction about the direction of the relationship between variables. In other words, it predicts whether the relationship is positive or negative. For example, “Students who spend more time studying will score higher on tests.”

Directional hypotheses are often used when previous research or theory suggests a specific outcome.

4. Non-Directional Hypothesis

A non-directional hypothesis predicts that there will be a relationship between the variables but does not specify the direction of the relationship. For instance, “There is a relationship between study time and test scores.” Non-directional hypotheses are useful when the researcher is unsure whether the variables are positively or negatively correlated.

5. Complex Hypothesis

A complex hypothesis involves more than two variables and predicts the relationships among them. For example, “Increased study time and use of study aids will result in higher test scores.” Complex hypotheses are common in social science research, where multiple factors often interact to influence outcomes.

How to Formulate a Hypothesis

Formulating a strong hypothesis requires careful thought and consideration of existing knowledge and research. Here are some steps to guide you through the process:

1. Identify the Research Question

The first step in formulating a hypothesis is to identify a research question. This is the broader question you are trying to answer through your study. For example, “What factors influence student test scores?”

2. Conduct a Literature Review

A thorough review of the existing literature helps you understand what is already known about the topic. This step allows you to build on previous research and avoid duplicating studies. It also helps you identify gaps in the literature that your research could fill.

3. Identify the Variables

Next, determine which variables you want to study. In our example, the variables are “study time” and “test scores.” Make sure your variables are measurable and observable.

4. Make an Educated Guess

Based on the literature review and your understanding of the topic, make a prediction about how the variables are related. This prediction forms the basis of your hypothesis. For instance, you might predict that “students who study more will perform better on tests.”

5. Ensure Testability

Finally, ensure that your hypothesis is testable. This means you need to be able to collect data and analyze it to either support or reject your hypothesis.

Testing a Hypothesis

Once a hypothesis is formulated, the next step is to test it. This typically involves collecting data and analyzing it to determine whether the hypothesis is supported. Researchers use various methods to test hypotheses, including experiments, surveys, and observational studies.

1. Data Collection

The method of data collection will depend on the nature of the hypothesis and the research design. For example, if the hypothesis predicts that increased study time leads to better test scores, the researcher could collect data through surveys, test scores, and time logs.

2. Statistical Testing

Statistical tests are used to determine whether the data support the hypothesis. For instance, a common method is to conduct a correlation analysis to examine the relationship between study time and test scores.

3. Interpretation of Results

Once the data have been analyzed, researchers interpret the results to determine whether they support or refute the hypothesis. If the data show a significant relationship between the variables, the hypothesis is supported. If no relationship is found, the hypothesis is rejected.

Hypothesis in the Context of Social Science

In social science, hypotheses are essential for developing new theories, testing existing theories, and exploring relationships between social phenomena. Because social science often deals with complex and multifaceted human behaviors, hypotheses in this field must account for a wide range of variables and external factors.

For instance, a social scientist studying education may hypothesize that smaller class sizes improve student performance. However, they must also consider other variables, such as teacher quality, socioeconomic status, and access to resources. In this way, social science hypotheses often involve multiple variables and interactions.

Hypothesis and Research Ethics

It is important to consider ethics when formulating and testing hypotheses. Ethical considerations ensure that research does not harm participants and that the research process is transparent and unbiased. Researchers should avoid forming hypotheses that could lead to biased or misleading conclusions. Additionally, they must ensure that their testing methods respect participants’ rights and privacy.

A hypothesis is a vital element in the research process. It provides a focused and testable prediction about the relationship between variables, guiding researchers through data collection and analysis. By formulating a clear and testable hypothesis, social scientists can explore complex social phenomena, test theories, and contribute to the advancement of knowledge in their field.

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What is a Hypothesis?

Table of Contents

Defining the hypothesis, the role of a hypothesis in the scientific method, types of hypotheses, hypothesis formulation, hypotheses and variables.

• The Importance of Testing Hypotheses
• The Hypothesis and Sociological Theory

In sociology, as in other scientific disciplines, the hypothesis serves as a crucial building block for research. It is a central element that directs the inquiry and provides a framework for testing the relationships between social phenomena. This article will explore what a hypothesis is, how it is formulated, and its role within the broader scientific method. By understanding the hypothesis, students of sociology can grasp how sociologists construct and test theories about the social world.

A hypothesis is a specific, testable statement about the relationship between two or more variables. It acts as a proposed explanation or prediction based on limited evidence, which researchers then test through empirical investigation. In essence, it is a statement that can be supported or refuted by data gathered from observation, experimentation, or other forms of systematic inquiry. The hypothesis typically takes the form of an “if-then” statement: if one variable changes, then another will change in response.

In sociological research, a hypothesis helps to focus the investigation by offering a clear proposition that can be tested. For instance, a sociologist might hypothesize that an increase in education levels leads to a decrease in crime rates. This hypothesis gives the researcher a direction, guiding them to collect data on education and crime, and analyze the relationship between the two variables. By doing so, the hypothesis serves as a tool for making sense of complex social phenomena.

The hypothesis is a key component of the scientific method, which is the systematic process by which sociologists and other scientists investigate the world. The scientific method begins with an observation of the world, followed by the formulation of a question or problem. Based on prior knowledge, theory, or preliminary observations, researchers then develop a hypothesis, which predicts an outcome or proposes a relationship between variables.

Once a hypothesis is established, researchers gather data to test it. If the data supports the hypothesis, it may be used to build a broader theory or to further refine the understanding of the social phenomenon in question. If the data contradicts the hypothesis, researchers may revise their hypothesis or abandon it altogether, depending on the strength of the evidence. In either case, the hypothesis helps to organize the research process, ensuring that it remains focused and methodologically sound.

In sociology, this method is particularly important because the social world is highly complex. Researchers must navigate a vast range of variables—age, gender, class, race, education, and countless others—that interact in unpredictable ways. A well-constructed hypothesis allows sociologists to narrow their focus to a manageable set of variables, making the investigation more precise and efficient.

Sociologists use different types of hypotheses, depending on the nature of their research question and the methods they plan to use. Broadly speaking, hypotheses can be classified into two main types: null hypotheses and alternative (or research) hypotheses.

Null Hypothesis

The null hypothesis, denoted as H0, states that there is no relationship between the variables being studied. It is a default assumption that any observed differences or relationships are due to random chance rather than a real underlying cause. In research, the null hypothesis serves as a point of comparison. Researchers collect data to see if the results allow them to reject the null hypothesis in favor of an alternative explanation.

For example, a sociologist studying the relationship between income and political participation might propose a null hypothesis that income has no effect on political participation. The goal of the research would then be to determine whether this null hypothesis can be rejected based on the data. If the data shows a significant correlation between income and political participation, the null hypothesis would be rejected.

Alternative Hypothesis

The alternative hypothesis, denoted as H1 or Ha, proposes that there is a significant relationship between the variables. This is the hypothesis that researchers aim to support with their data. In contrast to the null hypothesis, the alternative hypothesis predicts a specific direction or effect. For example, a researcher might hypothesize that higher levels of education lead to greater political engagement. In this case, the alternative hypothesis is proposing a positive correlation between the two variables.

The alternative hypothesis is the one that guides the research design, as it directs the researcher toward gathering evidence that will either support or refute the predicted relationship. The research process is structured around testing this hypothesis and determining whether the evidence is strong enough to reject the null hypothesis.

The process of formulating a hypothesis is both an art and a science. It requires a deep understanding of the social phenomena under investigation, as well as a clear sense of what is possible to observe and measure. Hypothesis formulation is closely linked to the theoretical framework that guides the research. Sociologists draw on existing theories to generate hypotheses, ensuring that their predictions are grounded in established knowledge.

To formulate a good hypothesis, a researcher must identify the key variables and determine how they are expected to relate to one another. Variables are the factors or characteristics that are being measured in a study. In sociology, these variables often include social attributes such as class, race, gender, age, education, and income, as well as behavioral variables like voting, criminal activity, or social participation.

For example, a sociologist studying the effects of social media on self-esteem might propose the following hypothesis: “Increased time spent on social media leads to lower levels of self-esteem among adolescents.” Here, the independent variable is the time spent on social media, and the dependent variable is the level of self-esteem. The hypothesis predicts a negative relationship between the two variables: as time spent on social media increases, self-esteem decreases.

A strong hypothesis has several key characteristics. It should be clear and specific, meaning that it unambiguously states the relationship between the variables. It should also be testable, meaning that it can be supported or refuted through empirical investigation. Finally, it should be grounded in theory, meaning that it is based on existing knowledge about the social phenomenon in question.

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What is the Scientific Method?

• Scientific method: a systematic, organized series of steps that ensures maximum objectivity and consistency in researching a problem
• Five basic steps of the scientific method: o Defining the problem o Reviewing the literature o Formulating the hypothesis o Selecting the research design and then collecting and analyzing data o Developing the conclusion

Defining the Problem

• The first step in a research project is to define the problem as clearly as possible
• Operational definition: an explanation of an abstract concept that is specific enough to allow a researcher to assess the concept o For example, for research purposes education could be defined as the number of years of schooling a person has achieved, and earnings as the income a person reports having received in the past year

Reviewing the Literature

• A review of the literature involves examining the relevant scholarly studies and information
• This allows researchers to: o Refine the problem under study o Clarify possible techniques to be used in data collection o Eliminate or reduce avoidable mistakes

Formulating the Hypothesis

• Hypothesis: a speculative statement about the relationship between two or more factors known as variables
• Variable: a measurable trait or characteristic subject to change under different conditions o Independent variable: the variable hypothesized to cause or influence another o Dependent variable: the variable whose action depends on the influence of the independent variable
• Casual logic: involves the relationship between a condition or variable and a particular consequence, with one leading to the other
• Correlation: exists when a change in one variable coincides with a change in the other o Correlation does not necessarily indicate causation
• Sociologists seek to identify the casual link between variables

Collecting and Analyzing Data

Sample: a selection from a larger population that is statistically representative of that population

Random sample: when every member of an entire population being studied has the same chance of being selected

Snowball or convenience samples: participants are recruited through word of mouth or by posting notices on the internet

The scientific method requires that research results be both valid and reliable

Validity: the degree to which a measure or scale truly reflects the phenomenon being studied

Reliability: refers to the extent to which a measure produces consistent results

Developing the Conclusion

• The conclusion of a research study represents an end and a beginning o Additional research questions or ideas should be generated from a study’s conclusions
• Sociological studies do not always generate data that support the original hypothesis
• Control variable: a factor held constant in order to test the relative impact of an independent variable

In Summary: The Scientific Method

• Defining the problem
• Reviewing the literature
• Formulating a hypothesis
• Collecting and analyzing data
• Developing the conclusion

Major Research Designs

• Research design: a detailed plan or method for obtaining data scientifically o Selection is often based on the theories and hypotheses the researcher starts with
• Types of research designs: o Surveys o Ethnography / observational o Experiments o Existing sources / archival
• Survey: a study, generally an interview or questionnaire, that provides sociologists with information about how people think and act
• Interview: a researcher obtains information through fact-to-face, phone, or online questioning
• Questionnaire: a researcher uses a printed or written form to obtain information from a respondent
• Survey questions must be worded carefully
• Researchers must pay attention to changes in society o For example, relationship questions in the Census must now take into consideration the possibility of same-sex partners
• Surveys are an example of quantitative research

Ethnography

Ethical Difficulties

• Because most sociological research uses people as sources of information, delicate ethical questions must be asked
• For example, a sociologist who is engaged in participant-observation research should protect the subjects’ confidentiality

Confidentiality

• Confidentiality involves keeping information secret
• Rik Scarce Case: o Scarce was conducting research on environmental protesters o He was jailed in 1993 for refusing to answer questions to a grand jury about an animal rights activist raid on a university research laboratory o He spent 159 days in jail o The ASA supported Scarce’s position

Conflict of Interest

• Accepting funds from a private organization or even a government agency that stands to benefit from a study’s results can call into question a researchers objectivity and integrity
• Exxon Corporation’s research on jury verdicts: o A federal jury had ordered Exxon to pay $5 billion in damages for the Valdez accident o The company approached legal scholars, sociologists, and psychologists to study jury deliberations to develop
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Introduction to Hypothesis tests

An explantion of the principles of Hypothesis testing - a key idea in statistics

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is most often used by researchers to test predictions, called hypotheses.

Null and Alternative Hypotheses

The first step in the hypothesis testing process is to frame your research question in terms of the data that you will collect. You want to think about what statement you are trying to test.

You then want to think about how the data will look different if that statement is true/false. To do this, we state null and alternative hypotheses . These are two competing statements.

The null hypothesis (called H0) usually follows the format that “there is no difference between these numbers” or “there is no relationship between variables”. 

The alternative hypothesis (called H1) is a statement that is the opposite of the null hypothesis and usually follows the format that “there is a difference between these two numbers” or “there is a relationship between variables”.

A hypothesis test is where we examine the data and decide which of the two alternative hypotheses is more believable given the evidence we have.

We begin by assuming the null hypothesis is true.

Does attending MASH statistics workshops have an impact on attainment in quantitative modules?

• Null Hypothesis: The mean module mark for students who attend MASH workshops is the same as for students who do not attend MASH workshops.
• Alternative hypothesis: The mean module mark for students who attend MASH workshops is different to the marks for students who do not attend MASH workshop.

If the results for some students who attended MASH statistics workshops and some students who did not attend MASH workshops are as follows:

From this information alone we can see that the mean mark for students who attended MASH workshops is higher than for students who did not. However, is the difference large enough to be significant? A hypothesis test is a way of approaching questions like this question formally and consistently, rather than just looking at the difference between the numbers and deciding whether we think it counts as a “big” difference

Carrying out a hypothesis test

The main steps for carrying out significance/hypothesis test are:

• Calculate the test statistic (calculate a single value which represents the important feature of the data we’re testing, for example, a mean) using the data collected
• Use the test statistic to compare what we have observed to what we would expect under the null hypothesis
• Use test statistic to obtain a p-value & use this to decide whether to reject the null hypothesis or not

What is a P-value?

Definition: The probability of observing a result (test statistic) at least as extreme as the one calculated, if the null hypothesis is true.

To understand the p-value it’s helpful to think about it as the probability of seeing a difference/relationship/results as ‘big’ as the one calculated if the null hypothesis is true.  If we have a p-value that is small, this means that the probability of seeing that difference when the null hypothesis is true is also small. Therefore the null hypothesis is unlikely to be true. The smaller the p-value the less likely the null hypothesis is to be true, so we have more evidence to reject the null hypothesis.

In order to have enough evidence to reject the null hypothesis, we want our p-value to be as small as possible.

The p-value is almost always calculated using a computer. It is possible to learn to use the formulas which calculate p-values by hand but we won’t discuss the mathematics here. The p-value depends on the sample size, the test statistic and the spread (usually standard deviation) of the samples.

Let’s return to the example above.

Our null hypothesis is:

“The mean module mark for students who attend MASH workshops is the same as for students who do not attend MASH workshops.”

If our results were:

We can see that we don’t have evidence that the null hypothesis is false. In this case the 0 is our test statistic and the associated p-value would be large.

The test statistic is 4 - the two samples were not exactly the same. The p-value would tell us how likely it is to get this result (or a more extreme result) if the reality is that MASH workshops have no effect on marks. The p-value would be smaller than above.

We can see a bigger difference between the means. If MASH workshops don’t make a difference, this result is more unlikely than either of the two above examples. Therefore, the p-value would now be smaller again. This would mean we’re more likely to doubt our null hypothesis.

The Significance Level

From the discussion above, we begin to see that the smaller the p-value is, the less likely we are to believe the null hypothesis. We need a threshold for how small our p-value should be before we decide not to believe the null hypothesis.

We call this threshold the significance level and give it the greek letter α (alpha).

We say that our result is statistically significant if the p-value is less than the significance level (α) . Strictly speaking, you can decide for yourself how big you would like the significance level to be. Very often, the significance level is set at 0.05. 

If the p-value is less than the significance level, we say we have statistical significance and we say we have “evidence to reject the null hypothesis”.

By convention, we never say that we “accept” a hypothesis. This is because it’s always technically possible for either hypothesis to be true, even if the p-value is very small. However, if we have very small p-values, we can talk about “strong evidence to reject the null” or “very strong evidence to reject the null”.

The definition of a significance level is often given as:

The significance level (α) is the probability of rejecting the null hypothesis when it is actually true.

This ties up with the idea of a significance level as a threshold for a “small” p-value because the p-value is the probability of getting a result like the one we got if the null hypothesis is true and we reject the null hypothesis if the p-value is smaller than the significance level. 

Type 1 and Type 2 Errors

When we carry out a hypothesis test there are four different outcomes:

• The null hypothesis is true and we correctly decide to not reject the null hypothesis
• The null hypothesis is false and we correctly decide to reject the null hypothesis
• The null hypothesis is true and we incorrectly decide to reject the null hypothesis. This is known as a Type 1 error .
• The null hypothesis is false and we incorrectly decide to not reject the null hypothesis. This is known as a Type 2 error .

If we have carried out our data collection and other elements of the research properly, we will usually find ourselves in scenario 1 or 2 - ie. the data we have collected will reflect reality and we will get a correct result. However, scenarios 3 and 4 can still occur and we have to understand why.

The Type 1 error is a type of error whereby we are declaring that there is a difference between groups, when actually there is no true difference. The type 1 error rate is equal to the significance level (α) - ie. if the significance level is 0.05 (5%) then we will get a type 1 error 5% of the time. A Type 1 error is sometimes called a “false positive” result

The probability of (correctly)  rejecting the null hypothesis when it is actually false is called the Power of the study. It is the probability of concluding that there is a difference, when a difference truly exists.

A Type 2 error is where the data look as though there is no difference between groups (ie. it seems as though the null hypothesis is true) but in reality, there is a difference. The probability of this happening is labelled (beta). If we know that there is a difference and we know how big the difference is, we can calculate . However, the whole point of the hypothesis test is to find out if a difference actually exists so we don’t usually get to calculate outside of examples made up for textbook exercises.

Let’s look at this in the context of our example from above. The possibilities are described in the table below.:

Remember that even though there are four different possibilities, they are not all equally likely.

As discussed in the previous section, if the difference between the means is larger, we would expect the p-value to be smaller, but the p-value also depends on factors such as spread and sample size.

One and Two Tailed Tests

There are two ways of conducting any hypothesis test, namely one tailed and two tailed. In a one tailed test, we only look for a difference in one direction. In a two tailed test, we would look for a difference in both directions.

For example, if we want to know if people on a diet have lost weight we might do a one-tailed test to see if (weight before)-(weight after) is positive. 

If our diet actually made people gain weight, this would not show up as a significant result on a one tailed test. However, if we conducted a two tailed test, a difference in either direction would show up as significant.

Practically: software will usually perform two-tailed tests by default. Unless you have a good reason, just stick with the default two-tailed test.

Statistical Significance and Meaningful difference 

Statistical significance is concerned with whether the null hypothesis is true or not.

In the example we have used, our null hypothesis is the statement “the means of two different groups of people are the same”. A statistically significant result is evidence to say that there is a difference between the two means but if a difference exists, we haven’t said anything about how big the difference is. 

Large sample sizes and small standard deviations can lead to significant results even when the difference is small. Small sample sizes may not detect actual differences in the population. We could call a difference that is big enough to be worthwhile a “meaningful” difference. In medical contexts this is sometimes referred to as clinical significance. Meaningful significance is related to the idea of effect size. Statistical tools other than p-values (such as effect size and confidence intervals) will help you decide whether the difference is meaningful.

Useful Resources

There are lots of ways of describing hypothesis testing and many people find it useful to read or listen to a few explanations to begin with. When you find that you can see why all the different descriptions are actually all saying the same thing, you’ve probably got the idea!

Here’s a webpage with an explanation we liked to get you started.

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