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“Effect sizes” as a book category covers works that explain how to quantify the magnitude and practical importance of research findings, moving beyond p-values to measures that are comparable across studies and contexts. Titles in this category introduce and compare key metrics (e.g., Cohen’s d, Hedges’ g, standardized mean differences, correlation r and R^2, odds and risk ratios, eta-squared and partial eta-squared), show how to compute confidence intervals and convert among measures, and demonstrate interpretation across designs (t-tests, ANOVA, regression/GLM, multilevel models). They guide readers in study planning (power and precision), evidence synthesis (meta-analysis, forest plots, heterogeneity, publication bias), and modern practices (robust and Bayesian estimation, equivalence and noninferiority tests, minimal important differences). Aimed at researchers, students, and practitioners in fields like psychology, education, medicine, and the social sciences, these books often include applied case studies, software tutorials (R, Python, SPSS, Stata), and reporting standards, emphasizing clear, reproducible communication of practical significance.

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