Which statistical test is appropriate for categorical data?

The Chi-square test is specifically designed to analyze categorical data by assessing the association between two or more categorical variables. It evaluates whether the observed frequencies of events or outcomes in different categories significantly differ from what would be expected if there were no association. This makes it an ideal choice when the data is nominal or ordinal in nature.

In contrast, other tests listed are not suitable for categorical data. The t-test is used for comparing means between two groups for continuous data, while ANOVA extends this concept to compare means across multiple groups. Regression analysis, on the other hand, is used for understanding relationships between independent and dependent variables, primarily within the context of continuous data. Therefore, for categorical data analysis, the Chi-square test stands out as the appropriate method.