Statistical Power Analysis, authored by Kevin Murphy and Brett Myors, is a comprehensive guide to understanding and applying statistical power analysis in both traditional null hypothesis tests and modern minimum-effect hypotheses. The book provides a simple and general model for statistical power analysis, based on the widely used F statistic, which can be applied to various statistical methods used in social and behavioral sciences. It covers topics such as the structure of statistical tests, the general linear model, effect size, and the implications of power analysis. The fifth edition includes updates to all chapters, new material, and recalculations of examples, along with new power software written in R. The book is intended for graduate students, researchers, and professionals in the behavioral and social sciences, as well as other fields where statistical methods are used. Key concepts include the importance of power in avoiding Type II errors, the role of sample size, effect size, and decision criteria in determining power, and the mechanics of power analysis. The authors also discuss the advantages of testing minimum-effect hypotheses and provide practical guidance on designing studies, interpreting results, and diagnosing existing research.Statistical Power Analysis, authored by Kevin Murphy and Brett Myors, is a comprehensive guide to understanding and applying statistical power analysis in both traditional null hypothesis tests and modern minimum-effect hypotheses. The book provides a simple and general model for statistical power analysis, based on the widely used F statistic, which can be applied to various statistical methods used in social and behavioral sciences. It covers topics such as the structure of statistical tests, the general linear model, effect size, and the implications of power analysis. The fifth edition includes updates to all chapters, new material, and recalculations of examples, along with new power software written in R. The book is intended for graduate students, researchers, and professionals in the behavioral and social sciences, as well as other fields where statistical methods are used. Key concepts include the importance of power in avoiding Type II errors, the role of sample size, effect size, and decision criteria in determining power, and the mechanics of power analysis. The authors also discuss the advantages of testing minimum-effect hypotheses and provide practical guidance on designing studies, interpreting results, and diagnosing existing research.