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“Multiple comparisons” is a statistical category focused on how to make valid inferences when many hypotheses, groups, outcomes, or models are evaluated simultaneously. Books in this area explain why testing multiple effects inflates false positives and describe methods to control error rates, from classic family-wise procedures (e.g., Bonferroni, Holm, Tukey/Scheffé post-hoc tests) to modern false discovery rate approaches (e.g., Benjamini–Hochberg), selective/post-selection inference, and Bayesian or empirical-Bayes shrinkage. Typical coverage includes experimental design and post-hoc analysis, power and sample size planning under multiplicity, strategies to avoid p-hacking, and practical applications in fields like clinical trials, psychology, genomics, and online A/B testing, often with software examples in R, Python, or SAS. These books blend theory with hands-on guidance to support transparent, reproducible statistical practice.

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