Which test is used for analyzing two independent samples of nominal data?

The Chi-square test is utilized for analyzing two independent samples of nominal data because it assesses whether there is a significant association between two categorical variables. Nominal data refers to categories without any intrinsic ordering (like gender, color, or yes/no responses), and the Chi-square test evaluates if the distribution of sample data across categories differs from what would be expected by chance alone.

When dealing with two independent samples, the Chi-square test specifically allows researchers to determine if the frequencies of occurrences in different groups—such as different treatment groups or demographic categories—are significantly different from each other. This is crucial in many fields, including social sciences and medicine, where researchers often need to compare categorical outcomes across different groups.

In contrast, the other tests mentioned serve different purposes. The T-test and paired T-test are used for comparing means between groups, particularly with interval or continuous data, while ANOVA extends this comparison when you have more than two groups. As such, these tests do not apply to the analysis of nominal data in the same manner as the Chi-square test does.