Which hypothesis testing method is suitable for evaluating the relationship between author gender and literary genre?

The Chi-squared (χ²) test is ideal for evaluating the relationship between categorical variables, such as author gender (a categorical variable with categories like male, female, and potentially non-binary) and literary genre (also a categorical variable with categories like fiction, non-fiction, poetry, etc.). This test assesses whether there is a significant association between the two categorical variables, allowing researchers to understand if the distribution of one variable differs depending on the levels of the other variable.

In this context, you would typically create a contingency table that displays the frequency counts of authors by genre and gender. The Chi-squared test then calculates whether the observed frequencies differ significantly from the expected frequencies under the null hypothesis, which posits that there is no relationship between the two variables.

Other methods listed, such as A/B tests, t-tests, and ANOVA, focus on comparing means or proportions when dealing with continuous data or two groups rather than determining relationships between categorical variables. A/B tests are generally used for comparing two different scenarios or groups, t-tests compare the means of two groups, and ANOVA extends this comparison to three or more groups. Hence, they are not suitable for examining the relationship between categorical variables like gender and genre.