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“Multiple testing” as a book category focuses on statistical principles and methods for making reliable inferences when many hypotheses are evaluated at once. These books explain how simultaneous testing inflates false positives and introduce error-control frameworks such as family-wise error rate (FWER), false discovery rate (FDR), and related metrics, along with classical and modern procedures (e.g., Bonferroni, Holm/Hochberg, Benjamini–Hochberg, q-values, permutation/resampling, empirical Bayes, selective inference, and online/adaptive approaches). Typical coverage includes theory and computation, dependence among tests, high-dimensional settings, power and sample-size planning, multiplicity in experimental design, and simultaneous inference for model parameters and post hoc comparisons. Readers will find practical workflows with software (often in R and Python) and case studies from genomics, neuroimaging, clinical trials, social sciences, and digital A/B testing. The category serves statisticians, data scientists, and applied researchers seeking both rigorous foundations and hands-on tools to control errors, prioritize discoveries, and enhance reproducibility in large-scale analyses.

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