Which statistical test is used for determining whether two nominal data distributions differ significantly?

The Chi-square test is the appropriate choice for assessing whether two nominal data distributions differ significantly. This statistical method evaluates the relationship between categorical variables by comparing the observed frequencies of occurrences in different categories to the expected frequencies that would occur if there were no association between the variables.

In its application, the Chi-square test helps to determine if the proportions of categories in one group are different from those in another group, making it particularly effective for analyzing nominal data, which consists of categories without any inherent order, such as gender or preferences.

While the other tests have their specific applications, they are not designed for nominal data. The Kruskal-Wallis Test and Mann-Whitney U Test are non-parametric tests used primarily for ordinal data or continuous data that do not follow a normal distribution. Multiple regression is a predictive analysis tool that examines the relationship between a dependent variable and multiple independent variables, which is not applicable for merely comparing the distributions of nominal data.