For which test is it appropriate to analyze nominal data regarding group differences?

The chi-square test is specifically designed for analyzing nominal data, particularly when assessing the differences or associations between categorical variables. This statistical method evaluates whether the observed frequencies in different categories significantly differ from the expected frequencies under the null hypothesis.

In contexts involving nominal data—such as gender, race, or other categories where the values do not have a specific order—chi-square tests can effectively determine whether there is a relationship between two categorical variables. This makes it appropriate for examining group differences, such as whether the distribution of preferences, occurrences, or characteristics is the same across different groups.

Other analytical methods, like ANOVA, regression analysis, and t-tests, are intended for different types of data. ANOVA is used for comparing means across multiple groups and typically requires interval or ratio data. Regression analysis is focused on predicting a continuous outcome from one or more predictor variables and involves continuous data. T-tests are used to compare means between two groups that have interval or ratio level data. Therefore, the chi-square test stands out as the right choice for nominal data analysis regarding group differences.