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A Political Science Guide

For students, researchers, and others interested in doing the work of political science, formulating/extracting hypotheses.

Formulating hypotheses, which are defined as propositions set forth to explain a group of facts or phenomena, is a fundamental component to any research scholarship. Hypotheses lay out the central arguments that will be tested and either verified or rejected in the body of a paper. Papers may address multiple competing or supporting hypotheses in order to account for the full spectrum of explanations that could account for the phenomenon being studied. As such, hypotheses often include statements about a presumed impact of an independent variable on a dependent variable.

Hypotheses should not emanate from preconceived perceptions about a given relationship between variables, but rather should come about as a product of research. Thus, hypotheses should be formed after developing an understanding of the relevant literature to a given topic rather than before conducting research. Beginning research with a specific argument in mind can lead to discounting other evidence that could either run counter to this preconceived argument or could point to other potential explanations.

There are a number of different types of hypotheses utilized in political science research:

• Null hypothesis: states that there is no relationship between two concepts
• Correlative hypothesis: states that there is a relationship, between two or more concepts or variables, but doesn’t specify the nature of a relationship
• Directional hypothesis: states the nature of the relationship between concepts or variables. These types of relationships can include positive, negative (inverse), high or low levels of influence, etc.
• Causal hypothesis: states that one variable causes the other

A good hypothesis should be both correlative and directional and most hypotheses in political science research will also be causal, asserting the impact of an independent variable on a dependent variable.

There are a number of additional considerations that must be taken into account in order to make a hypothesis as strong as possible:

• Hypotheses  must be falsifiable , that is able to be empirically tested. They cannot attribute causation to something like a supernatural entity whose existence can neither be proven nor denied.
• Hypotheses must be internally consistent , that is that they must be proving what they claim to be proving and must not contain any logical or analytical contradiction
• Hypotheses must have clearly defined outcomes (dependent variables) that are both dependent and vary based on the dependent variable.
• Hypotheses must be general and should aim to explain as much as possible with as little as possible. As such, hypotheses should have as few exceptions as possible and should not rely on amorphous concepts like ‘national interest.’
• Hypotheses must be empirical statements that are propositions about relationships that exist in the real world.
• Hypotheses must be plausible (there must be a logical reason why they might be true) and should be specific (the relationship between variables must be expressed as explicitly as possible) and directional.
• Fearon, James D. 1991. Counterfactuals and Hypothesis Testing in Political Science . World Politics 43 (2): 169-195.

Abstract : “Scholars in comparative politics and international relations routinely evaluate causal hypotheses by referring to counterfactual cases where a hypothesized causal factor is supposed to have been absent. The methodological status and the viability of this very common procedure are unclear and are worth examining. How does the strategy of counterfactual argument relate, if at all, to methods of hypothesis testing based on the comparison of actual cases, such as regression analysis or Mill’s Method of Difference? Are counterfactual thought experiments a viable means of assessing hypotheses about national and international outcomes, or are they methodologically invalid in principle? The paper addresses the first question in some detail and begins discussion of the second. Examples from work on the causes of World War I, the nonoccurrence of World War III, social revolutions, the breakdown of democratic regimes in Latin America, and the origins of fascism and corporatism in Europe illustrate the use, problems and potential of counterfactual argument in small-N-oriented political science research.” – Jstor.org

• King, Gary, Robert Owen Keohane, and Sidney Verba. 1994. Designing social inquiry: scientific inference in qualitative research. Princeton, NJ: Princeton University Press.
• Palazzolo, David and Dave Roberts. 2010. What is a Good Hypothesis? University of Richmond Writing Center.

Contributor: Harrison Polans

updated July 12, 2017 – MN

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University Libraries

Psci 3300: introduction to political research.

• Library Accounts
• Selecting a Topic for Research
• From Topic to Research Question
• From Question to Theories, Hypotheses, and Research Design
• Annotated Bibliographies
• The Literature Review
• Search Strategies for Ann. Bibliographies & Lit. Reviews
• Find PSCI Books for Ann. Bibliographies & Lit. Reviews
• Databases & Electronic Resources for Your Lit. Review
• Methods, Data Analysis, Results, Limitations, and Conclusion
• Finding Data and Statistics for the Data Analysis
• Citing Sources for the Reference Page

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Hypothesis in Political Science

"A generalization predicting that a relationship exists between variables. Many generalizations about politics are a sort of folklore. Others proceed from earlier work carried out by social scientists. Within the social sciences most statements about behaviour relate to large groups of people. Hence, testing any hypothesis in the field of political science will involve statistical method. It will be dealing with probabilities.

To test a hypothesis one must pose a null hypothesis. If we wanted to test the validity of the common generalization, 'manual workers tend to vote for the Labour Party' we would begin by assuming the statement was untrue. The investigation would require a sample survey in which manual workers were identified and questions put to them. It would need to be done in several constituencies in different parts of the country. Having collated the data we would use the evidence to test the null hypothesis, employing statistical techniques to assess the probability of acquiring such data if the null hypothesis were correct. These techniques are known as 'significance tests'. They estimate the probability that the rejection of a null hypothesis is a mistake. If the statistical tests indicates that the odds against it being a mistake are 1000 to one, then this is stated as a '.001 level of significance'.

The fact that the research showed that it was highly likely that manual workers 'tend' to vote for the Labour vote would not satisfy most political scientists. They also want to understand those who did not. Consequently much more work would need to be done to refine the hypothesis and define the tendency with more accuracy. Whatever the case, a hypothesis in the social sciences about a group or socio-demographic category can never tell us about the behaviour of an individual in that group or category."

Hypothesis. (1999). In F. Bealey. The Blackwell Dictionary of Political Science , Oxford, United Kingdom: Blackwell Publishers.

What a Quantitative Research Design?

Quantitative research studies produce results that can be used to describe or note numerical changes in measurable characteristics of a population of interest; generalize to other, similar situations; provide explanations of predictions; and explain causal relationships. The fundamental philosophy underlying quantitative research is known as positivism, which is based on the scientific method of research. Measurement is necessary if the scientific method is to be used. The scientific method involves an empirical or theoretical basis for the investigation of populations and samples. Hypotheses must be formulated, and observable and measurable data must be gathered. Appropriate mathematical procedures must be used for the statistical analyses required for hypothesis testing.

Quantitative methods depend on the design of the study (experimental, quasi-experimental, non-experimental). Study design takes into account all those elements that surround the plan for the investigation, such as research question or problem statement, research objectives, operational definitions, scope of inferences to be made, assumptions and limitations of the study, independent and dependent variables, treatment and controls, instrumentation, systematic data collection actions, statistical analysis, time lines, and reporting procedures. The elements of a research study and experimental, quasi-experimental, and nonexperimental designs are discussed here.

Elements of Quantitative Design

Problem statement.

First, an empirical or theoretical basis for the research problem should be established. This basis may emanate from personal experiences or established theory relevant to the study. From this basis, the researcher may formulate a research question or problem statement.

Operational Definitions

Operational definitions describe the meaning of specific terms used in a study. They specify the procedures or operations to be followed in producing or measuring complex constructs that hold different meanings for different people. For example, intelligence may be defined for research purposes by scores on the Stanford-Binet Intelligence Scale.

Population and Sample

Quantitative methods include the target group (population) to which the researcher wishes to generalize and the group from which data are collected (sample). Early in the planning phase, the researcher should determine the scope of inference for results of the study. The scope of inference pertains to populations of interest, procedures used to select the sample(s), method for assigning subjects to groups, and the type of statistical analysis to be conducted.

Formulation of Hypotheses

Complex questions to compare responses of two or more groups or show relationships between  two or more variables are best answered by hypothesis testing. A hypothesis is a statement of the researcher's expectations about a relationship between variables.

Hypothesis Testing

Statements of hypotheses may be written in the alternative or null form. A directional alternative hypothesis states the researcher's predicted direction of change, difference between two or more sample means, or relationship among variables. An example of a directional alternative hypothesis is as follows:

Third-grade students who use reading comprehension strategies will score higher on the State Achievement Test than their counterparts who do not use reading comprehension strategies.

A nondirectional alternative hypothesis states the researcher's predictions without giving the direction of the difference. For example:

There will be a difference in the scores on the State Achievement Test between third-grade students who use reading comprehension strategies and those who do not.

Stated in the null form, hypotheses can be tested for statistically significant differences between groups on the dependent variable(s) or statistically significant relationships between and among variables. The null hypothesis uses the form of “no difference” or “no relationship.” Following is an example of a null hypothesis:

There will be no difference in the scores on the State Achievement Test between third-grade students who use reading comprehension strategies and those who do not.

It is important that hypotheses to be tested are stated in the null form because the interpretation of the results of inferential statistics is based on probability. Testing the null hypothesis allows researchers to test whether differences in observed scores are real, or due to chance or error; thus, the null hypothesis can be rejected or retained.

Organization and Preparation of Data for Analysis

Survey forms, inventories, tests, and other data collection instruments returned by participants should be screened prior to the analysis. John Tukey suggested that exploratory data analysis be conducted using graphical techniques such as plots and data summaries in order to take a preliminary look at the data. Exploratory analysis provides insight into the underlying structure of the data. The existence of missing cases, outliers, data entry errors, unexpected or interesting patterns in the data, and whether or not assumptions of the planned analysis are met can be checked with exploratory procedures.

Inferential Statistical Tests

Important considerations for the choice of a statistical test for a particular study are (a) type of research questions to be answered or hypotheses to be tested; (b) number of independent and dependent variables; (c) number of covariates; (d) scale of the measurement instrument(s) (nominal, ordinal, interval, ratio); and (e) type of distribution (normal or non-normal). Examples of statistical procedures commonly used in educational research are  t  test for independent samples, analysis of variance, analysis of covariance, multivariate procedures, Pearson product-moment correlation, Mann–Whitney  U  test, Kruskal–Wallis test, and Friedman's chi-square test.

Results and Conclusions

The level of statistical significance that the researcher sets for a study is closely related to hypothesis testing. This is called the alpha level. It is the level of probability that indicates the maximum risk a researcher is willing to take that observed differences are due to chance. The alpha level may be set at .01, meaning that 1 out of 100 times the results will be due to chance; more commonly, the alpha level is set at .05, meaning that 5 out of 100 times observed results will be due to chance. Alpha levels are often depicted on the normal curve as the critical region, and the researcher must reject the null hypothesis if the data fall into the predetermined critical region. When this occurs, the researcher must conclude that the findings are statistically significant. If the  researcher rejects a true null hypothesis (there is, in fact, no difference between the means), a Type I error has occurred. Essentially, the researcher is saying there is a difference when there is none. On the other hand, if a researcher fails to reject a false null (there is, in fact, a difference), a Type II error has occurred. In this case, the researcher is saying there is no difference when a difference exists. The power in hypothesis testing is the probability of correctly rejecting a false null hypothesis. The cost of committing a Type I or Type II error rests with the consequences of the decisions made as a result of the test. Tests of statistical significance provide information on whether to reject or fail to reject the null hypothesis; however, an effect size ( R 2 , eta 2 , phi, or Cohen's  d ) should be calculated to identify the strength of the conclusions about differences in means or relationships among variables.

Salkind, Neil J. 2010.  Encyclopedia of Research Design . Thousand Oaks, CA: SAGE Publications, Inc. doi: 10.4135/9781412961288 .

Some Terms in Statistics that You Should Know

Bivariate Regression

Central Tendacy, Measures of

Chi-Square Test

Cohen's d Statistic

Cohen's f Statistic

Correspondence Analysis

Cross-Sectional Design

Descriptive Statistics

Effect Size, Measure of

Eta-Squared

Factor Loadings

False Positive

Frequency Tables

Alternative Hypotheses

Null Hypothesis

Krippendorff's Alpha

Multiple Regression

Multivariate Analysis of Variance (MANOVA)

Multivariate Normal Distribution

Partial Eta-Squared

Percentile Rank

Random Error

Reliability 

Regression Discontinuity

Regression to the Mean

Standard Deviation

Significance, Statistical

Trimmed Mean

Variability, Measure of

Is the term you are looking for not here? Review the Encyclopedia of Research Design below. 

SAGE Research Methods is a research methods tool created to help researchers, faculty and students with their research projects. SAGE Research Methods links over 175,000 pages of SAGE’s renowned book, journal and reference content. Researchers can explore methods concepts to help them design research projects, understand particular methods or identify a new method, conduct their research, and write up their findings. Since SAGE Research Methods focuses on methodology rather than disciplines, it can be used across the social sciences, health sciences, and more. Subject coverage includes sociology, health, criminology, education, anthropology, psychology, business, political science, history, economics, among others.

Sage Research Methods has a feature called a Methods Map that can help you explore different types of Research Designs .

You can also explore Cases to see real research using your selected research method to learn how other authors are writing up their findings.

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Empirical Methods in Political Science: An Introduction

5 hypothesis testing.

By Zhihang Ruan

5.1 Introduction

Either in our daily lives or in scientific research, we come across a lot of claims. We may formulate our own hypotheses based on our knowledge, available information, or existing theory. These hypotheses can be descriptive, e.g., we may hypothesize that a certain percent of U.S. voters support the policy of universal basic income. Or the hypothesis can be causal, e.g., we may believe that education leads to stronger support for gender equality. The measures (for example, mean or standard deviation) used to describe a population distribution are called population parameter . If we have access to everyone among the population we are interested in, then we may easily tell whether our hypothesis of a population parameter is true or false (e.g., if we know every voter’s support for the policy of universal basic income, then we can prove/disprove our hypothesis concerning the support rate for the policy). But in many cases, we do not have access to the population to firmly prove or disprove our hypotheses. For example, it may cost too much to ask each U.S. voter about their opinions on specific policies. In these cases, statistical theory and methods provide us some effective ways to test a hypothesis, or more accurately, assess whether the observed data is or is not consistent with a claim of interest concerning the population. In this chapter, we will go through the idea of hypothesis testing in statistics and how it is applied in political science.

5.2 Background

There are different understandings of hypothesis testing. In this chapter, we will follow the Neyman-Pearson paradigm ( Rice 2007, 331 ) , which casts hypothesis testing as a decision problem. Within this paradigm, we first have a null hypothesis and an alternative hypothesis concerning the population. A null hypothesis is a claim or hypothesis we plan to test, or more specifically, something we decide whether to reject or not. It can be descriptive (e.g., the support rate for the president among all U.S. voters) or causal (education leads to stronger support for gender equality among all human beings). An alternative hypothesis is also called the research hypothesis, which is opposite to the null hypothesis. It is what we believe to be true if we reject the null hypothesis. Then with what we observe in a random sample from the population, we make a decision to reject or not reject the null hypothesis concerning the population. This approach does not enable us to say the exact probability that the null or alternative hypothesis is true. 7 To do that, we need more information and maybe another paradigm (e.g., so-called prior probability within the Bayesian paradigm), and we will not go in details in this chapter. But, even though the approach we discuss in this chapter does not directly tell us how likely a hypothesis is true or false, the approach is very useful in scientific studies as well as daily lives, as you will see in this chapter.

As mentioned in the introduction of this chapter, the classic idea of hypothesis testing concerns a sample and a population. In the previous chapter, we learned what the terms population, random sample and random sampling mean. The techniques we discuss in this chapter mostly assume a random sample. Below, we will quickly review the idea of random sampling and random sample and explains how random sampling enables us to make inference about the population with what we observe in the sample.

5.3 Samples and Sampling

As mentioned in the beginning of this chapter, in many cases, we do not have the access to all the units of the population we are interested in. For example, if we are interested in the support rate for the president, it would be perfect if we know the opinion of every single person (i.e., unit of the population) in the U.S. However, it is almost impossible to get access to everyone’s opinion. In many cases, we can only get access to a small group of individuals, which we call a sample from the population. When the sample is randomly chosen from the population (i.e., everyone in the population has an equal chance to be selected, or at least has a specific chance known to the researchers before the sample is drawn), then we may learn about the population with what we observe in the random sample we have. More specifically, statistical theory enables us to make inference about the population from the random sample. In the next part, I will explain how we may make inference from a random sample to the population and test a hypothesis concerning the population with a random sample.

5.3.1 Magic of the Central Limit Theorem

Let’s say, we roll a fair die. We know the probability of getting 1 is 1/6. In other words, the probability that the mean of the number we get from one trial equals 1 is 1/6. Then, if we roll the same die twice, we get two numbers. We can calculate the mean of the two numbers. What is the probability that the mean equals 1? Is the probability still 1/6? No, because if the mean is 1, we have to get 1 twice, the probability of which would be 1/36 (which equals 1/6 times 1/6). Very likely, the mean we get is larger than 1. Similarly, if we roll the die three times, the mean of the three numbers we get would probably be larger than 1. If we roll the die many times (e.g. 1,000 times), it is almost impossible that the mean would be 1 or even close to 1 (since it means we need to get 1 in all or most of the trials). Then what would the mean be? The mean would not be an extreme number like 1 or 6. Instead, it would be very close to the expected value we get from rolling it once, which is 3.5, the average of all possible numbers we get. Among the 1,000 trials, the number of 1s we get would be close to the amount of 2s we get, or the amount of 3s, etc. If we take the average of all numbers we get in the 1,000 trials, we would get a number very close to 3.5, which equals (1+2+3+4+5+6)/6.

This is what we call the weak law of large numbers: the sample average converges in probability towards the expected value or the population average, or in other words, the average of the sample gets close to the population average when the sample size is large (e.g., when rolling the die 1000 times).

One step further from the law of large numbers, we can rely on something called the central limit theorem to make inference. The central limit theorem suggests that the mean of a sufficiently large number of independent draws from any distribution will be normally distributed. A normal distribution is a bell-shaped probability density. From the example above, we already know the mean of a large amount of draws is very close to the expected value of the population. But in most cases, the average of the draws will not be exactly equal to the expected value of the population (which is 3.5 in the example of rolling a fair die). The central limit theorem enables us to calculate/quantify the probability that the sample average falls into intervals around the expected value of the population. As long as the expected value and variance of a normal distribution is known, we can calculate the probability that we get a sample mean within a specified interval. For example, with some calculation based on the central limit theorem (which we will not go into details here), we know that if we roll a fair die 1,000 times, the chance that the mean of the 1,000 numbers we get falls between 3.394 and 3.606 is roughly 0.95 (or 95 percent).

What if, after rolling the die 1,000 times, the average of the 1,000 numbers we get is much smaller than 3.394 or much larger than 3.606? Then we may want to check whether there is some problem with the rolling process, or whether the die is fair. Similarly, if we hypothesize that the support rate for the president is 50 percent, but after interviewing 1,000 people randomly drawn from the population, we find that the support rate is much lower than 50 percent, then we may doubt whether the support rate is really 50 percent. This makes sense when the sample is drawn randomly from the population. But if the sample is not drawn randomly (e.g., all the people in the sample are drawn from a gathering of a specific party), then the result does not tell us much about the support rate among the population. This is like a magician who uses tricks and gets 1 every time rolling a fair die. We cannot learn anything about the die based on the mean the magician gets.

These examples show us how central limit theorem works and how it makes hypothesis testing possible. In the next part, I will explain more specifically how we may estimate the population average/expected value based on what we observe from the sample, as well as how to test a hypothesis.

5.4 Estimates and Certainty

Based on the central limit theorem, we can make inferences about the population with the data we observe. One way to estimate the population parameter is called point estimate , which is a sample statistic used to estimate the exact value of a population parameter. We may consider the point estimate as our best guess to the population parameter based on what we observe in the sample. For example, if we learn that the mean of a random sample from simple random sampling is 3.5, then we may say that the point estimate of the population mean is 3.5.

But in most cases, the point estimate does not equal the true value of the population parameter (e.g., the population mean can be 3.5001, 3.4986 or other number when the sample mean is 3.5). Another way to estimate the population parameter is interval estimation. With the information we learn from the sample, we may calculate an interval that may include the population average. The central limit theorem enables us to quantify how confident we are that the interval will include the population average. The interval is called confidence interval , which defines a range of values within which the population parameter is estimated to fall. If we want to estimate the confidence interval of the population mean, we need the sample mean, the estimated population variance, and the sample size. A 95 percent confidence interval for the population mean equals \(\bar{X}\pm 1.96 * (S_{\bar{X}})\) . \(S_{\bar{X}}\) is the estimated standard error of the sampling distribution of the sample mean. It is equal to the standard error (or the square root of the variance) of the population divided by the square root of the sample size. 8 We can see from the formula that the range of the interval will decrease when the population variance is small, and the sample size is large. This makes sense intuitively because when there is little variation among the population, or when we have a large sample, the sample mean may be close to the population mean, and thus our estimation will be more precise.

In short, we can estimate the confidence interval of the population mean based on the sample we get. Similarly, if we have a hypothesis about the population average, then we can calculate an interval which the sample mean may fall into, and quantify how confident we are that the sample average will fall onto this interval.

It is intuitive to say that if we increase the range of our estimated interval, we are more confident that the interval will include the population mean. The trade-off is that our estimation is less precise. The likelihood, expressed as a percentage or a probability, that a specified interval will contain the population parameter, is called confidence level . For example, if we learn from a random sample (with a sample size of 1,000) that the support rate for the president is 52 percent, then a 95 percent confidence interval of the support rate among the population is between 50.5 and 53.5. And a 99 percent confidence interval is roughly 50.0 to 54.0 percent. As we can see, the confidence interval becomes wider (in other words, our estimation becomes less precise) if we want to be more confident that the population mean is within the confidence interval we estimate (i.e., we have a higher confidence level). More specifically, a 99 percent confidence interval for the population mean equals \(\bar{X}\pm 2.58 * (S_{\bar{X}})\) . 9 As we can see, the interval is wider than the 95 percent confidence interval, which is \(\bar{X}\pm 1.96 * (S_{\bar{X}})\) , and the 90 percent confidence interval, which is \(\bar{X}\pm 1.64 * (S_{\bar{X}})\) .

5.5 Steps of Hypothesis Testing

Hypothesis testing becomes more straightforward once we understand the central limit theorem and confidence interval. As mentioned earlier, if we have a hypothesis of the population mean, then we can calculate a confidence interval that the sample average will fall into. But if the sample average is very different from the population average we hypothesize, or in other words, falls outside the confidence interval at a specific confidence level, then we may reject the null hypothesis with a specific level of confidence. For example, if we hypothesize a die is a fair one, then the expected value (or the population mean) we get from rolling the die once is 3.5. However, if we roll the die many times (e.g., 1000 times), and the mean of all the numbers we get is 2.003, then we may be very confident to say that the die is not a fair die (i.e., we will reject the null hypothesis that the die is a fair one).

More specifically, there are four steps of hypothesis testing. First, we need to have a statement about a population parameter evaluated with a test statistic. The parameter can be the population mean (e.g., the average number of basketball games Americans go to), proportion (e.g., the support rate for the president among all U.S. voters), or some other characteristics of the population, like the variance of heights among all first-grade children. Any statement concerning the population implies a null hypothesis and an alternative/research hypothesis concerning the population. The research hypothesis is the hypothesis we’re putting forward to test, which reflects the substantive hypothesis. It is also called ‘alternative hypothesis’, but some prefer ‘research’ to convey that this hypothesis comes from an understanding of the subject area and is often derived from theory. The research/alternative hypothesis is in contrast to the null hypothesis , which is the ‘default’ one that we wish to challenge. For example, if most people believe that on average individuals in the U.S. go to more than 1 basketball game annually, and we hypothesize that on average Americans go to fewer than 1 basketball game every year. Then we can set our hypothesis as the research hypothesis and the common belief as the null hypothesis.

Then, we collect a random sample, calculate the statistic from the sample, and compare the statistic with the null hypothesis and the alternative hypothesis. What kind of statistic is calculated depends on the kind of hypothesis we have and statistical methods we use in hypothesis testing. For example, if we are interested in the population mean, then we need to calculate the mean and standard error of the sample.

Then we determine the rejection of the null hypothesis or of failure to reject the null. If the statistic we observe differs significantly from what we hypothesize, then we will reject the null hypothesis. Otherwise, we fail to reject the null hypothesis. As stated earlier, in most cases what we get from the sample is different from what we state in the null hypothesis. But we reject the null hypothesis only when what we observe in the sample is really weird or significantly different from the null hypothesis. What counts as weird, depends on the rule we set before, as well as common practices in the field. In social science, we usually take a pretty strict standard concerning the rejection of the null hypothesis. In many cases, only when the sample mean is outside the 95 percent, 99 percent or 99.9 percent of the confidence interval, do we reject the null hypothesis. This means, that we would expect to get a result as ‘weird’ as ours less than 5% of the time, if the null hypothesis is true. Since the probability is so low (e.g., 0.05), we reject the null hypothesis.

We tend to be conservative and decide not to reject the null hypothesis. Thus, failing to reject the null hypothesis does not mean the hypothesis is true, but just means that we do not have enough evidence to reject it. Similarly, rejecting the null hypothesis does not mean we prove that it is false, but it only suggest that we have pretty strong evidence (that we feel confident about) that it is false, if all the assumptions of the sampling process and statistical methods we use are met (e.g., the sample is a random sample from the population).

5.6 Types of Hypothesis testing

In our lives, we may have different types of claims or hypotheses. It can be a hypothesis about the mean of the population (e.g., the support rate for the president, the average income, etc.) or the variance of the population (e.g., the variance of people’s income). Or it can be a hypothesis concerning the difference between two groups, or a hypothesis about the correlation between two variables. Statisticians have developed different tests for different types of hypotheses. In this section, we will introduce some basic methods of hypothesis testing.

5.6.1 Single Mean Hypothesis Testing

The single mean hypothesis testing concerns the mean of the population we care about. In many cases, we are interested in the population average. For example, in an election, we may want to know the support rate for a specific candidate, which is important for the development of campaign strategy. We may hypothesize that the support rate for the candidate is a specific number, and we can test the hypothesis with a random sample we get from the population. If the support rate for the candidate among the sample is very different from the rate stated in our hypothesis, we may reject the hypothesis. If the rate we get from the sample is not very different from the number stated in the hypothesis, we may fail to reject the hypothesis.

Here is how a single mean hypothesis works. As we have discussed, the central limit theorem suggests that the mean of a random sample with a sufficiently large sample size is normally distributed. The normal distribution of the sample mean is an example of sampling distribution , which is a theoretical distribution of all possible sample values for the statistic which we are interested. For example, when we have a sample (with the sample size of 1,000), we can calculate the sample mean. If we do the sampling multiple times (e.g., 1 million times), we get 1 million samples and 1 million sample means (each sample still has 1000 cases). From the central limit theorem, we know that the 1 million sample means follow a normal distribution. This distribution is the sampling distribution of the sample mean, for samples with the sample size of 1,000.

If we get a simple random sample (explained in the previous chapter), the expected value of the sampling distribution of the mean equals the population mean, and the variance of the sampling distribution is determined by the population variance and the size of the sample. When there is less variation among the population, or we have a larger sample, the variance of the sampling distribution is smaller, which means the sample mean is expected to be closer to the population mean.

Since the sampling distribution of the mean is a normal distribution, we can calculate the probability that the sample mean falls into a specific range given the hypothesis is true. If the sample mean we get is very different from the hypothesized population mean, we may think there is some problem with the null hypothesis and we may reject the null hypothesis. Statisticians have learned a lot about the normal distribution, and we know that if we randomly draw a number from a normal distribution, we have roughly 95 percent chance of getting a number within two (or more accurately, 1.96) standard deviations (which equals the square root of the variance) away from the expected value of the normal distribution. Since the sampling distribution of the sample mean is a normal distribution, the chance that the distance between the sample mean we observe and the expected value of the normal distribution is more than two standard deviations of the normal distribution is roughly 5 percent. Thus, if we observe a difference between the sample mean and the hypothesized population mean that is larger than twice the standard deviations of the sampling distribution, we may reject the null hypothesis at the significance level of 95 percent. It is weird (e.g., less than 5 percent chance) to get a sample mean as extreme as the one we have if the null hypothesis is true, so we decide to reject the null hypothesis. We can also set a stricter standard (e.g., a significance level of 99 percent, or 99.9 percent) and reject the null only when the difference between the sample mean and the population mean is more extreme.

5.6.2 Difference of Means Hypothesis Testing

Sometimes, we are not interested in the mean of a single group, but more interested in the difference of means between two groups. Testing the difference of means is especially useful when we aim to make causal inference with an experiment. It can also be useful when we compare two groups without aiming to make causal inference. For example, in an election, especially an election within the majority system, we may be interested in whether one candidate has a higher support rate than another candidate. In this case, we are dealing with a hypothesis concerning the difference of means. The hypothesis may take the forms of \(A>B\) , \(A<B\) , \(A=B\) , or \(A-B=c\) . If our research hypothesis is \(A>B\) , the null hypothesis would be \(A<B\) . Then we test the hypothesis with what we observe in the random sample. For example, if the null hypothesis is that Candidate A has a higher support rate than Candidate B and we get a random sample in which Candidate A has a support rate much lower than Candidate B, then we may reject the hypothesis.

Similar to the single mean test, testing the difference of means hypothesis requires the standard deviation of the sampling distribution. We observe the difference of means among the two samples (groups), and then compare the difference to the standard deviation of the sampling distribution. If the difference is much larger than (e.g. more than two times) the standard deviation, then we may reject the null hypothesis that there is no difference between the two groups and suggest that there is statistically significant difference between the two groups.

5.6.3 Regression Coefficients Hypothesis Testing

In other cases, we are not only interested in describing the population, but analyzing the correlations of different variables concerning the population. We may want to test whether two characteristics or variables within the population are correlated with each other. To test the correlations, we may put them into a regression model, which we will discuss more in later chapters on regressions. Here we can briefly explain how testing regression coefficients works.

A bivariate regression model is like this. \[Y=\beta_{0} + \beta_{1} X\] If there is no correlation between a variable X and another variance Y, then any change of X will not be correlated to any change of Y. Thus, \(\beta_{1}\) in the regression model should be 0, which implies the value of Y will not change with the value of X. When we do the hypothesis testing, the null hypothesis is that the coefficient is 0. Then we put the data we get from a random sample into the regression model. The model will provide us an estimate of the coefficient. Then we do statistic tests (e.g., \(t\) test which compares the difference with the standard deviation) to see whether the coefficient estimated differs significantly from 0. If it differs significantly from 0, we may reject the null hypothesis and suggest that there may be some correlation between X and Y.

5.6.4 Conclusions you can draw based on the type of test

Based on the type of tests we conduct, we may draw certain types of conclusions. For example, with the single mean test, we may reject the null hypothesis that the single mean is a specific number or within a specific interval. With the test of the difference of means, we may reject that the null hypothesis that there is no difference between two groups. Based on the test of the regression coefficient, we may reject the null hypothesis that there is no correlation between two variables. But as stated above, in many cases we may fail to reject the null hypothesis. This does not suggest the null hypothesis is true, but that we do not have strong enough evidence to reject it.

5.7 Applications

The single mean hypothesis testing is very straightforward in statistics and one of the basic tools in social science research. Once we get a random sample and get the sample mean and sample variance, we can easily estimate the confidence interval for the population mean, e.g., the public opinions on specific policies. Then we can compare the null hypothesis with the sample mean or the confidence interval, and decide whether to reject the null hypothesis or not. The main challenge in these descriptive works is not statistical theory or method per se, but the sampling process. As we emphasize earlier in this chapter, to make inference about the population with a sample, we need to first have a random sample from the population, otherwise it is like trying to make inference based on magicians’ tricks. But it is extremely difficult to get a random sample in real lives. Many factors, like the non-response rate, lack of access to specific groups, financial and time constraints, make it unlikely to get a perfect random sample from the population. Researchers have tried different techniques to get a representative and random sample from the population. To test whether a sampling method is reliable, one way is to compare the findings we get with the new technique with census data or others authoritative data. In an article by Ansolabehere and Schaffner, they compare three sampling techniques (over the Internet, by telephone with live interviews, and by mail) with other data sources ( Ansolabehere and Schaffner 2014 ) . Comparing the confidence interval estimated from the sample with validating source, provides us some inputs on whether the sampling process provides a good enough (though not perfect) sample.

Testing hypotheses concerning the difference of means and regression coefficients are even more widely used in political science. In most studies in political science nowadays, researchers care about correlations or causal relations between different variables. Different methods, like regression and experiments, have been developed to explore the relations between different variables in the world, e.g., democracy and economic growth ( Boix and Stokes 2003 ) , social network and welfare provision ( Tsai 2007 ) , media frame strategy and public opinion ( Bonilla and Mo 2018 ) , etc. In these works which aim to explore relations between different variables, we often have a null hypothesis that there are no correlations between two variables, and researchers aim to find strong evidence to reject the null hypothesis.

More specifically, in an experiment, the null hypothesis is often that there are no difference between the treatment group and the control group. If we find statistically significant difference in the means between the treatment and the control groups, we may reject the null hypothesis and suggest that there are some difference between the two groups. And since the two groups differ in getting the treatment or not, researchers may suggest that the treatment is the cause for the difference between the two groups. Here is an example for of an experiment. As some may know, the general support for aid to foreign countries is low among U.S. citizens. This is a descriptive finding. But what explains the low support? Some researchers ( Scotto et al. 2017 ) suggest, one reason is that people in the United States and other developed countries tend to overestimate the percent of government budget spend on overseas aid. To test this research hypothesis, they designed an experiment in the United States and Great Britain, in which one group of people (i.e., the control group) are provided the amount of dollars/pounds spent on foreign aid each year, and the other group (i.e., the treatment group) of people are provided the amount of money as well as the percentage of government budget on overseas aid. Then they ask the two groups of people about their opinions on foreign aid, and test the difference of means between the two groups. They find out that the group of people informed the percentage as well as the amount of overseas aid are less likely to think that the governments have spent "too much" on foreign aid. The difference is statistically significant at the confidence level of 99 percent, which enables them to reject the null hypothesis that there are no difference between the two groups and argue that overestimating the percentage of budget spent on aid is one cause for the low support for foreign aid.

In many cases, we cannot randomly assign people into different groups and change the treatment they get. Other techniques, like regression discontinuity designs (RDD), may be used for testing whether there are differences between groups that were similar before the treatment. For example, some researchers are interested in whether advantaged individuals may see the world through the lens of the poor after engagement with disadvantaged populations ( Mo and Conn 2018 ) . To do that, they surveyed top college graduates who were accepted into Teach For America program and those who were not. The former group of students had selection scores just above the threshold score and the later group had scores fall just short of the threshold score. Since the two groups differed only slightly in the scores, so it may be reasonable to suggest that the two groups were similarly to each other, and then we can see whether the experience in the program changes how the students view the world.

When we use regressions based on observational data instead of experiments, the idea of hypothesis testing is similar. Researchers often have a null hypothesis that the coefficient for a specific variable \(X\) is 0, which implies no correlations between the explanatory variable \(X\) and the outcome variable \(Y\) . If from the sample we find that the estimated coefficient differs significantly from 0, then we may decide to reject the null hypothesis and suggest that there is some correlation between \(X\) and \(Y\) . Whether the correlation implies causal relations, requires a closer look on the research design, but is not something hypothesis testing can tell. For example, a study explores the correlation between anti-Muslim hostility and the support for ISIS in Western Europe; on Twitter, ISIS followers who are in constituencies with high vote shares for far-right parties are more likely to support ISIS. But the correlation does not necessarily mean that anti-Muslim hostility causes the support, and thus the researcher looks closer into the tweets before and after major events related to ISIS to show that the support is indeed linked to the anti-Muslim hostility ( Mitts 2019 ) . Another example is from the field of American politics; a researcher tests whether people whose family members are arrested or incarcerated become mobilized to vote or not ( A. White 2019 ) .

5.8 “Is it weird?”

The idea of hypothesis testing can be formulated as some kind of "Is it weird" question. We start from a hypothesis concerning the population, then we observe the data from a sample, and ask ourselves, someone with training in statistical methods, "is it weird that we get a sample like this, if the null hypothesis is true?" If it is weird (AKA statistically unlikely), in the sense of statistical method, then we will reject the null hypothesis. Otherwise, we decide not to reject the null hypothesis, though that does not mean we prove or accept the null hypothesis.

5.9 Broader significance/use in political science

The Neyman-Pearson paradigm of hypothesis testing may be a bit obscure if we have not gone through the idea behind it. Students without a firm understanding of the statistical theory behind may make mistakes when interpreting the result of hypothesis testing. In recent years, there have been some heated discussions on whether we should continue this paradigm and use some jargon with this paradigm, e.g., \(p\) value, statistical significance, et al. ( Ziliak and McCloskey 2008 ; Amrhein, Greenland, and McShane 2019 ) . One concern with this paradigm is whether we should set a threshold value (e.g., the confidence level of 95 percent) to reject the null hypothesis and suggest there is statistically significant correlation once the threshold is met, since this may mislead someone without much training in statistical methods to think that we are more than 95 percent confident that the alternative hypothesis is true. 10 Another concern is that the paradigm of hypothesis testing may not tell us much about substantial relationship. When the sample size is very large, it may be very easy to reject the null hypothesis and suggest that one variable may have statistically significant correlation with another variable, but the effect/correlation may be trivial. 11 Besides, the paradigm may bring the problem of publication bias. Researchers and journal editors may tend to report findings that show statistically significant correlations, but not findings that do not show significant correlations. This may make our understanding of the world biased.

Other than that, for studies that do not involve random sampling, how the Neyman-Pearson paradigm of hypothesis testing works is not very clear. For example, when we have a sample which is not randomly drawn from the population, we cannot test a hypothesis concerning the population with the sample we have. And if we have access to information concerning every unit of the population (e.g., if the unit of interest is country, then in many cases we get access to the whole population as long as we learn specific information of all countries in the world), what hypothesis testing means and how the method we introduced above tells us about the population is less clear.

Other paradigms of hypothesis testing, like Bayesian approach, may provide more intuitive ways for us to understand and explain hypothesis testing and quantitative results to new learners and the general public. But these paradigms are not necessarily incompatible with the paradigm introduced in this chapter. The main issue is when we use this approach of hypothesis testing, we should be clear what each step and the results mean, and what we can and cannot say with our findings.

5.10 Conclusion

Hypothesis testing is a basic tool in contemporary political science studies, especially in quantitative political science. In the following chapters, we will introduce specific methods that explore the relations between different variables in our society. Hypothesis testing is the basic idea behind most of these methods. Understanding how hypothesis testing works will make it easier for us to understand experiments, large-N analysis and other quantitative methods.

5.11 Application Questions

Before an election, a political analyst argues that the support rate for a candidate is above 60 percent. With a sample from all voters (assuming the sample is a random one), researchers find that the 95 percent confident interval of the support rate for the candidate is between 56.2 percent and 58.9 percent. Does this provide strong evidence that the analyst is wrong? Why or why not?

In an experiment, 80 students are randomly divided into two groups. The first group of students are asked to read a news article on the negative effects of climate change on peasants in developing countries, and the other group of students are asked to read an article on a new electronic device. Then both groups of students are asked about their opinions on the role of the United States in fighting climate change. Researchers find compared to the second group, the first group of students show slightly higher support for the U.S. government to take more responsibility in fighting climate change, but the difference is not statistically significant at the level of 95 percent. Does it mean that reading the news article on climate change has no effects on students’ opinions on U.S.’s responsibility in fighting climate change? Why or why not?

A student is interested in the average amount of courses Northwestern undergrads took last quarter. In total, there were 8,231 Northwestern undergrads last quarter. With a random sample from all NU undergrads, whose sample size is 196, she learned that on average, a student took 4.0 courses last quarter. With the sample, she estimated that the population variance is 1.21. Can you calculate a 95 percent confidence interval for the average amount of courses Northwestern undergrads took last quarter?

5.12 Key Terms

Central Limit Theorem

confidence interval

null hypothesis

population parameter

point estimate

quantitative data

random sample

regression coefficient

research hypothesis

standard deviation

standard error

statistically significant difference

5.13 Answers to Application Questions

Yes. This provides strong evidence that the analyst is wrong. The confidence interval of the support rate among the population suggests that we are 95 confident that the support rate will not be higher than 58.9 percent or lower than 56.2 percent. Since the prediction of the analyst (higher than 60 percent) is well beyond the confidence interval we calculated from the random sample, we are pretty confident the prediction is wrong. But this is based on assumptions that the sample is a random one, respondents in the survey tell their true preference for the candidate, etc. If these assumptions are not met, the sample does not tell us anything about the population and we cannot tell whether the analyst is right or wrong.

Finding no statistically significant difference between the two groups makes us fail to reject the null hypothesis, which is that there are no difference between the two groups. However, it does not tell us that the null hypothesis is true. We can only say that we do not find enough evidence to show that there are difference between the two groups based on one study, but we cannot say the difference is exactly 0.

A 95 confidence interval is \(\bar{X}\pm 1.96 * (S_{\bar{X}})\) . The sample mean is 4.0. The estimated standard error of the sampling distribution equals the square root of the population variance divided by the square root of the sample size, which is \(\sqrt{1.21}/\sqrt{196}=0.0785\) . Thus the 95 confidence interval is \(\bar{X}\pm 1.96 * (S_{\bar{X}}) = 4.0\pm 1.96* 0.0785= [3.846, 4.154]\) .

This may be a bit confusing. But you may consider it this way. Let’s say, we hypothesize that the average height of all Northwestern undergrads is 5.7 feet. If we do the hypothesis testing as we will learn in this chapter, we will not reject the null hypothesis unless we get a random sample whose average height is much higher than 5.7 or much lower than 5.7 feet. In many cases, we may not reject the hypothesis. However, how likely is the hypothesis true, even if we do not reject it? Almost 0, because the exact average height can be any number slightly different from 5.7 feet, e.g., 5.700001 or 5.697382. As a result, the hypothesis is almost always wrong, but we do not always reject it. Thus, whether to reject the hypothesis or not does not tell us whether it is true or false. Nor does it tell us the probability that it is true. ↩︎

We have 1.96 in the formula because statisticians tell us if we randomly draw a number from a normal distribution, we have a 95 percent chance of getting a number no more than 1.96 standard errors above or below the mean of the distribution. ↩︎

We have 2.58 in the formula because if we randomly draw a number from a normal distribution, we have a 99 percent chance of getting a number no more than 2.58 standard errors above or below the mean of the distribution. ↩︎

As I have tried to explain, the level of significance is not the probability that the research hypothesis is true. ↩︎

For example, the finding that 1 million investment in education for one student may increase her annual income by 100 dollars after graduation may be statistically significant, but the effect is too small to tell any substantial relations. ↩︎

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

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

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

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

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

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

Null Hypothesis

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

Alternative Hypothesis

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

Directional Hypothesis

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

Non-directional Hypothesis

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

Statistical Hypothesis

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

Composite Hypothesis

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

Empirical Hypothesis

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

Simple Hypothesis

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

Complex Hypothesis

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

Applications of Hypothesis

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

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

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

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

Conduct a Literature Review

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

Determine the Variables

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

Formulate the Hypothesis

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

Write the Null Hypothesis

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

Refine the Hypothesis

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

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

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

Purpose of Hypothesis

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

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

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

When to use Hypothesis

Here are some common situations in which hypotheses are used:

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

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

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

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

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

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

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

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POLSC101: Introduction to Political Science

Research in political science.

This handout is designed to teach you how to conduct original political science research. While you won't be asked to write a research paper, this handout provides important information on the "scientific" approach used by political scientists. Pay particularly close attention to the section that answers the question "what is scientific about political science?"

If you were going to conduct research in biology or chemistry, what would you do? You would probably create a hypothesis, and then design an experiment to test your hypothesis. Based on the results of your experiment, you would draw conclusions. Political scientists follow similar procedures. Like a scientist who researches biology or chemistry, political scientists rely on objectivity, data, and procedure to draw conclusions. This article explains the process of operationalizing variables. Why is that an important step in social science research?

Defining politics and political science

Political scientist Harold Laswell said it best: at its most basic level, politics is the struggle of "who gets what, when, how". This struggle may be as modest as competing interest groups fighting over control of a small municipal budget or as overwhelming as a military stand-off between international superpowers. Political scientists study such struggles, both small and large, in an effort to develop general principles or theories about the way the world of politics works. Think about the title of your course or re-read the course description in your syllabus. You'll find that your course covers a particular sector of the large world of "politics" and brings with it a set of topics, issues, and approaches to information that may be helpful to consider as you begin a writing assignment. The diverse structure of political science reflects the diverse kinds of problems the discipline attempts to analyze and explain. In fact, political science includes at least eight major sub-fields:

• American politics examines political behavior and institutions in the United States.
• Comparative politics analyzes and compares political systems within and across different geographic regions.
• International relations investigates relations among nation-states and the activities of international organizations such as the United Nations, the World Bank, and NATO, as well as international actors such as terrorists, non-governmental organizations (NGOs), and multi-national corporations (MNCs).
• Political theory analyzes fundamental political concepts such as power and democracy and foundational questions, like "How should the individual and the state relate?"
• Political methodology deals with the ways that political scientists ask and investigate questions.
• Public policy examines the process by which governments make public decisions.
• Public administration studies the ways that government policies are implemented.
• Public law focuses on the role of law and courts in the political process.

What is scientific about political science?

Investigating relationships

Although political scientists are prone to debate and disagreement, the majority view the discipline as a genuine science. As a result, political scientists generally strive to emulate the objectivity as well as the conceptual and methodological rigor typically associated with the so-called "hard" sciences (e.g., biology, chemistry, and physics). They see themselves as engaged in revealing the relationships underlying political events and conditions. Based on these revelations, they attempt to state general principles about the way the world of politics works. Given these aims, it is important for political scientists' writing to be conceptually precise, free from bias, and well-substantiated by empirical evidence. Knowing that political scientists value objectivity may help you in making decisions about how to write your paper and what to put in it.

Political theory is an important exception to this empirical approach. You can learn more about writing for political theory classes in the section "Writing in Political Theory" below.

Building theories

Since theory-building serves as the cornerstone of the discipline, it may be useful to see how it works. You may be wrestling with theories or proposing your own as you write your paper. Consider how political scientists have arrived at the theories you are reading and discussing in your course. Most political scientists adhere to a simple model of scientific inquiry when building theories. The key to building precise and persuasive theories is to develop and test hypotheses. Hypotheses are statements that researchers construct for the purpose of testing whether or not a certain relationship exists between two phenomena. To see how political scientists use hypotheses, and to imagine how you might use a hypothesis to develop a thesis for your paper, consider the following example. Suppose that we want to know whether presidential elections are affected by economic conditions. We could formulate this question into the following hypothesis: "When the national unemployment rate is greater than 7 percent at the time of the election, presidential incumbents are not reelected".

Collecting data

In the research model designed to test this hypothesis, the dependent variable (the phenomenon that is affected by other variables) would be the reelection of incumbent presidents; the independent variable (the phenomenon that may have some effect on the dependent variable) would be the national unemployment rate. You could test the relationship between the independent and dependent variables by collecting data on unemployment rates and the reelection of incumbent presidents and comparing the two sets of information. If you found that in every instance that the national unemployment rate was greater than 7 percent at the time of a presidential election the incumbent lost, you would have significant support for our hypothesis.

However, research in political science seldom yields immediately conclusive results. In this case, for example, although in most recent presidential elections our hypothesis holds true, President Franklin Roosevelt was reelected in 1936 despite the fact that the national unemployment rate was 17%. To explain this important exception and to make certain that other factors besides high unemployment rates were not primarily responsible for the defeat of incumbent presidents in other election years, you would need to do further research. So you can see how political scientists use the scientific method to build ever more precise and persuasive theories and how you might begin to think about the topics that interest you as you write your paper.

Clear, consistent, objective writing

Since political scientists construct and assess theories in accordance with the principles of the scientific method, writing in the field conveys the rigor, objectivity, and logical consistency that characterize this method. Thus political scientists avoid the use of impressionistic or metaphorical language, or language which appeals primarily to our senses, emotions, or moral beliefs. In other words, rather than persuade you with the elegance of their prose or the moral virtue of their beliefs, political scientists persuade through their command of the facts and their ability to relate those facts to theories that can withstand the test of empirical investigation. In writing of this sort, clarity and concision are at a premium. To achieve such clarity and concision, political scientists precisely define any terms or concepts that are important to the arguments that they make. This precision often requires that they "operationalize" key terms or concepts. "Operationalizing" simply means that important – but possibly vague or abstract – concepts like "justice" are defined in ways that allow them to be measured or tested through scientific investigation.

Fortunately, you will generally not be expected to devise or operationalize key concepts entirely on your own. In most cases, your professor or the authors of assigned readings will already have defined and/or operationalized concepts that are important to your research. And in the event that someone hasn't already come up with precisely the definition you need, other political scientists will in all likelihood have written enough on the topic that you're investigating to give you some clear guidance on how to proceed. For this reason, it is always a good idea to explore what research has already been done on your topic before you begin to construct your own argument. (See our handout on making an academic argument.)

Example of an operationalized term

To give you an example of the kind of "rigor" and "objectivity" political scientists aim for in their writing, let's examine how someone might operationalize a term. Reading through this example should clarify the level of analysis and precision that you will be expected to employ in your writing. Here's how you might define key concepts in a way that allows us to measure them.

We are all familiar with the term "democracy". If you were asked to define this term, you might make a statement like the following: "Democracy is government by the people". You would, of course, be correct – democracy is government by the people. But, in order to evaluate whether or not a particular government is fully democratic or is more or less democratic when compared with other governments, we would need to have more precise criteria with which to measure or assess democracy. Most political scientists agree that these criteria should include the following rights and freedoms for citizens:

• Freedom to form and join organizations
• Freedom of expression
• Right to vote
• Eligibility for public office
• Right of political leaders to compete for support
• Right of political leaders to compete for votes
• Alternative sources of information
• Free and fair elections
• Institutions for making government policies depend on votes and other expressions of preference

By adopting these nine criteria, we now have a definition that will allow us to measure democracy. Thus, if you want to determine whether Brazil is more democratic than Sweden, you can evaluate each country in terms of the degree to which it fulfills the above criteria.

What counts as good writing in political science?

While rigor, clarity, and concision will be valued in any piece of writing in political science, knowing the kind of writing task you've been assigned will help you to write a good paper. Two of the most common kinds of writing assignments in political science are the research paper and the theory paper.

Writing political science research papers

Your instructors use research paper assignments as a means of assessing your ability to understand a complex problem in the field, to develop a perspective on this problem, and to make a persuasive argument in favor of your perspective. In order for you to successfully meet this challenge, your research paper should include the following components: (1) an introduction, (2) a problem statement, (3) a discussion of methodology, (4) a literature review, (5) a description and evaluation of your research findings, and (6) a summary of your findings. Here's a brief description of each component.

In the introduction of your research paper, you need to give the reader some basic background information on your topic that suggests why the question you are investigating is interesting and important. You will also need to provide the reader with a statement of the research problem you are attempting to address and a basic outline of your paper as a whole. The problem statement presents not only the general research problem you will address but also the hypotheses that you will consider. In the methodology section, you will explain to the reader the research methods you used to investigate your research topic and to test the hypotheses that you have formulated. For example, did you conduct interviews, use statistical analysis, rely upon previous research studies, or some combination of all of these methodological approaches?

Before you can develop each of the above components of your research paper, you will need to conduct a literature review. A literature review involves reading and analyzing what other researchers have written on your topic before going on to do research of your own. There are some very pragmatic reasons for doing this work. First, as insightful as your ideas may be, someone else may have had similar ideas and have already done research to test them. By reading what they have written on your topic, you can ensure that you don't repeat, but rather learn from, work that has already been done. Second, to demonstrate the soundness of your hypotheses and methodology, you will need to indicate how you have borrowed from and/or improved upon the ideas of others.

By referring to what other researchers have found on your topic, you will have established a frame of reference that enables the reader to understand the full significance of your research results. Thus, once you have conducted your literature review, you will be in a position to present your research findings. In presenting these findings, you will need to refer back to your original hypotheses and explain the manner and degree to which your results fit with what you anticipated you would find. If you see strong support for your argument or perhaps some unexpected results that your original hypotheses cannot account for, this section is the place to convey such important information to your reader. This is also the place to suggest further lines of research that will help refine, clarify inconsistencies with, or provide additional support for your hypotheses. Finally, in the summary section of your paper, reiterate the significance of your research and your research findings and speculate upon the path that future research efforts should take.

Writing in political theory

Political theory differs from other subfields in political science in that it deals primarily with historical and normative, rather than empirical, analysis. In other words, political theorists are less concerned with the scientific measurement of political phenomena than with understanding how important political ideas develop over time. And they are less concerned with evaluating how things are than in debating how they should be. A return to our democracy example will make these distinctions clearer and give you some clues about how to write well in political theory.

Earlier, we talked about how to define democracy empirically so that it can be measured and tested in accordance with scientific principles. Political theorists also define democracy, but they use a different standard of measurement. Their definitions of democracy reflect their interest in political ideals – for example, liberty, equality, and citizenship – rather than scientific measurement. So, when writing about democracy from the perspective of a political theorist, you may be asked to make an argument about the proper way to define citizenship in a democratic society. Should citizens of a democratic society be expected to engage in decision-making and administration of government, or should they be satisfied with casting votes every couple of years?

In order to substantiate your position on such questions, you will need to pay special attention to two interrelated components of your writing: (1) the logical consistency of your ideas and (2) the manner in which you use the arguments of other theorists to support your own. First, you need to make sure that your conclusion and all points leading up to it follow from your original premises or assumptions. If, for example, you argue that democracy is a system of government through which citizens develop their full capacities as human beings, then your notion of citizenship will somehow need to support this broad definition of democracy. A narrow view of citizenship based exclusively or primarily on voting probably will not do. Whatever you argue, however, you will need to be sure to demonstrate in your analysis that you have considered the arguments of other theorists who have written about these issues. In some cases, their arguments will provide support for your own; in others, they will raise criticisms and concerns that you will need to address if you are going to make a convincing case for your point of view.

Drafting your paper

If you have used material from outside sources in your paper, be sure to cite them appropriately in your paper. In political science, writers most often use the APA or Turabian (a version of the Chicago Manual of Style) style guides when formatting references. Check with your instructor if he or she has not specified a citation style in the assignment. For more information on constructing citations, see the UNC Libraries citation tutorial.

Although all assignments are different, the preceding outlines provide a clear and simple guide that should help you in writing papers in any sub-field of political science. If you find that you need more assistance than this short guide provides, refer to the list of additional resources below or make an appointment to see a tutor at the Writing Center.

Works consulted

We consulted these works while writing the original version of this handout. This is not a comprehensive list of resources on the handout's topic, and we encourage you to do your own research to find the latest publications on this topic. Please do not use this list as a model for the format of your own reference list, as it may not match the citation style you are using. For guidance on formatting citations, please see the UNC Libraries citation tutorial.

Becker, Howard S. 1986. Writing for Social Scientists: How to Start and Finish Your Thesis, Book, or Article . Chicago: The University of Chicago Press.

Cuba, Lee. 2002. A Short Guide to Writing about Social Science , Fourth Edition. New York: Longman.

Lasswell, Harold Dwight. 1936. Politics: Who Gets What, When, How . New York, London: Whittlesey House, McGraw-Hill Book Company, inc.

Scott, Gregory M. and Stephen M. Garrison. 1998. The Political Science Student Writer's Manual , Second Edition. Upper Saddle River, New Jersey: Prentice Hall, Inc.

Turabian, Kate L. 1996. A Manual for Writers of Term Papers , Theses, and Dissertations, Sixth Edition. Chicago: The University of Chicago Press.

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Definition of a Hypothesis

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

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

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

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

Null Hypothesis

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

Alternative Hypothesis

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

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

Formulating a Hypothesis

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

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

Updated by Nicki Lisa Cole, Ph.D

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