What is the purpose of the chi square test in social science research?

• A. The chi square test assesses association between categorical variables or goodness-of-fit between observed and expected frequencies.
• B. It compares means of two groups.
• C. It assesses correlation between continuous variables.
• D. It tests the normality of a distribution.

Answer: (A)

Chi-square tests focus on categorical data to see whether observed frequencies differ from what would be expected under a defined hypothesis. There are two main purposes. First, to test whether two categorical variables are independent (no association between them). For example, you might ask if gender is related to voting preference. Second, to test goodness-of-fit—whether the observed distribution of counts across categories matches a theoretical distribution or specified proportions, such as equal chances for each category or a hypothesized pattern.

In practice, you compare what you actually observed in your data to what you would expect under the null hypothesis, compute a chi-square statistic, and use that to assess significance. If the observed counts are notably different from the expected counts, you have evidence against the null hypothesis.

This method is specifically for categorical data. It isn’t used to compare means (that would be a t-test or ANOVA), it isn’t used to assess correlation between continuous variables, and it isn’t a test of normality.