This text investigates the critical role of effect sizes in statistical analysis, arguing that researchers must move beyond simple null-hypothesis significance testing to understand the magnitude and practical importance of their findings. Authors John J. Kim and Robert J. Grissom provide a comprehensive framework for calculating, interpreting, and reporting effect sizes across a wide array of univariate and multivariate statistical models. By synthesizing complex mathematical concepts into accessible methodologies, the authors equip researchers with the tools necessary to enhance the transparency and replicability of empirical studies.

Experts recognize this volume as a foundational reference for graduate students and professional researchers seeking to improve their statistical reporting practices. Readers frequently note the technical density of the prose, which requires a solid background in introductory statistics to fully grasp the presented material.