Statistical Power Analysis: A Simple and General Model for Traditional and Modern Hypothesis Tests

This book presents a simple and general method for conducting statistical power analysis based on the widely used F statistic. The book illustrates how these analyses work and how they can be applied to problems of studying design, to evaluate others' research, and to choose the appropriate criterion for defining "statistically significant" outcomes. Statistical Power Analysis examines the four major applications of power analysis, concentrating on how to determine:

*the sample size needed to achieve desired levels of power;

*the level of power that is needed in a study;

*the size of effect that can be reliably detected by a study; and

*sensible criteria for statistical significance.


Highlights of the second edition include: a CD with an easy-to-use statistical power analysis program; a new chapter on power analysis in multi-factor ANOVA, including repeated-measures designs; and a new One-Stop PV Table to serve as a quick reference guide.


The book discusses the application of power analysis to both traditional null hypothesis tests and to minimum-effect testing. It demonstrates how the same basic model applies to both types of testing and explains how some relatively simple procedures allow researchers to ask a series of important questions about their research. Drawing from the behavioral and social sciences, the authors present the material in a nontechnical way so that readers with little expertise in statistical analysis can quickly obtain the values needed to carry out the power analysis.


Ideal for students and researchers of statistical and research methodology in the social, behavioral, and health sciences who want to know how to apply methods of power analysis to their research.

"I recommend...[it] highly to biostatisticians, econometricians, and statisticians."
―Journal of Statistical Computation and Simulation

"This a useful introductory text to power analysis. It would be well suited to researchers who are involved in testing hypotheses on an everyday basis, but without a strong statistical background."
―Journal of the Royal Statistical Society

"...Murphy and Myors' ability to explain difficult or obscure concepts in an easy to understand style is what makes this text excellent....Students would find [this book] a refreshing approach to understanding and mastering a sometimes difficult task."
―Kim Ernst, Ph.D.
Loyola University

"I found it easy to read and understand--not my typical reaction to a book of this type."
―Joe Rosse, Ph.D.
University of Colorado at Boulder

"...I refer graduate students to it as they prepare their dissertation proposals....They turn to it for their research, and that is a very good sign."
―James W. Lichtenberg, Ph.D.
University of Kansas