A Handout on the Chi-Square Test

Chi-Square Test
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Chi-Square Test
A chi-square test (χ2) is a statistical technique that is used to compare observed results in research to the expected results. This is especially important in the study to determine whether there is a difference between the two and whether any such differences are due to underlying relationships or completely by chance (Verma & Abdel-Salam, 2019). Therefore, being a test of significance, the chi-square test only tries to illuminate the viability of the results but does not imply any causality between the variables. This type of test is mostly used in cases of categorical variables that are not numerical, such as eye color and gender.
The most commonly used types of chi-square test include the test for independence and the goodness of fit test. Both tests require a comparison of the test statistic calculated from the data and the chi-square distribution tables based on the degrees of freedom used. The test for independence is used to determine the likelihood of two variables being related or not (Verma & Abdel-Salam, 2019). For instance, a researcher wanting to analyze the relationship between productivity rates and time spent watching television can use the chi-square test of independence to determine the relationship. In this case, the null hypothesis will state that there is no relationship between productivity rates and the time spent watching television, while the alternative hypothesis will counter this claim. On the other hand, the goodness of fit tests determines the possibility that one type of categorical data under study comes from a theoretical distribution based on available ideas (Verma & Abdel-Sal...