When the data is categorical — pass/fail, treated/untreated, exposed/unexposed — you need tests designed for proportions, not means. And knowing that two variables are associated is rarely enough. You need to know where the association is strongest, how large the effect is, and whether the confidence interval on the risk difference or odds ratio is precise enough to act on.
Most packages offer a χ² test and stop there. No mosaic plots to see the pattern. No risk difference or odds ratio with proper score-based confidence intervals. No distinction between independent and related tables. No McNemar test for before-and-after designs. The categorical analysis is an afterthought — and the results reflect it.
See contingency table results in detail — independence tests, mosaic plots, proportion tests, and effect sizes with CIs.
Contingency table analysis is one part of a complete statistical analysis toolkit. The Standard edition also includes ANOVA and ANCOVA, simple and multiple regression, logistic regression, PCA and factor analysis, descriptive statistics, hypothesis testing, and correlation. See everything in the Standard edition →