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The Statistical Power category features books that explain and apply the concept of power—the probability that a study will detect a true effect when it exists—and its central role in sound research. Titles range from practical guides to advanced texts on power analysis and sample-size determination, covering effect sizes, alpha/beta trade-offs, Type I/II errors, multiple comparisons, clustered and longitudinal designs, mixed models, and simulation-based approaches. Readers learn how to plan adequately powered experiments and surveys, interpret and report power, and avoid common pitfalls like underpowered studies, p-hacking, and publication bias, with attention to ethics, cost, and reproducibility. Many books include step-by-step tutorials using software such as R, Python, SAS, Stata, SPSS, and G*Power. Applications span psychology, biomedicine and clinical trials, education, marketing, and public policy, making the category relevant to students, researchers, and data practitioners.

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