What statistical test is appropriate if a study involves both categorical variables and compares frequencies?

When dealing with a study that involves both categorical variables and seeks to compare frequencies, the Chi Square test is the appropriate statistical test to use. This test assesses whether there is a significant association between two categorical variables, allowing researchers to determine if the distribution of sample categorical data aligns with an expected distribution or if there are differences in frequencies across groups.

In this context, the Chi Square test calculates how observed frequencies compare to expected frequencies, providing insights into relationships or differences among categories. For example, if you were examining the relationship between gender and preference for a particular product, you would use the Chi Square test to analyze how many males and females prefer each product.

The other options are not suitable for this scenario: ANOVA is used primarily for comparing means across three or more groups when dealing with continuous variables, the t-test examines differences between the means of two groups, and the Mann-Whitney U Test is a non-parametric test for comparing differences between two independent groups when the data are not normally distributed, typically focusing on ranks rather than frequencies.