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The “False Discovery Rate” category gathers books that explain, develop, and apply methods for controlling false positives when testing many hypotheses at once. Centered on the idea that FDR is the expected proportion of spurious findings among declared discoveries, these works span foundational treatments of multiple testing (e.g., Benjamini–Hochberg procedures, q-values, empirical Bayes) and modern extensions (adaptive, hierarchical, and Bayesian approaches), as well as practical guides for fields like genomics, neuroimaging, social science, finance, and online experimentation. Readers learn how to design analyses, set thresholds, interpret and report q-values, implement workflows in common software, and avoid pitfalls such as p-hacking and selection bias. The category emphasizes reproducibility and robust inference, offering case studies and best practices for researchers, data scientists, and students working with large-scale data.

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