Which test is appropriate for related or paired samples in nominal data?

The appropriate test for related or paired samples in nominal data is the McNemar test. This test is specifically designed to analyze the differences between two related groups when the data is categorical. For example, it can be used when measuring the same subjects before and after a certain treatment, or in cases where there are matched pairs of subjects.

The McNemar test works by comparing the frequencies of two outcomes—typically a binary outcome such as "yes" or "no". It assesses whether there has been a change in responses in the paired samples. This makes it particularly suitable for nominal data where the possible values are categories rather than continuous measures.

Other tests listed serve different functions: the Chi-square test is used for independent samples typically with two or more categories but does not account for the paired nature of the samples. The T-test is intended for comparing means between groups with continuous data, and the Wilcoxon rank sum test is used for independent samples with ordinal or continuous data, rather than paired. Therefore, the McNemar test is uniquely suited for the analysis of paired nominal data.