Power calculations are rarely done by hand. Instead, researchers normally refer to tables of critical values in much the same way that tables of critical values for t, F, and other statistics were once used to assess statistical significance.

A far easier way to run a power analysis is to use a power calculator or a computer program such as G*Power (Faul et al. 2007). At the time of writing the latest version of this freeware program was G*Power 3 which runs on both Windows XP/Vista/7/8 and Mac OS X10.7 – 10.10 operating systems. This user-friendly program can be used to run all types of power analysis for a variety of distributions. Using the interface you select the outcome of interest (e.g., minimum sample size), indicate the test type, input the parameters (e.g., the desired power and alpha levels), then click “calculate” to get an answer.

For a step-by-step guide to G*Power 3, complete with screenshots, check out the e-book Statistical Power Trip:

Daniel Soper of Arizona State University has several easy-to-use calculators for all sorts of statistical calculations including power analyses relevant for multiple regression.

Russ Lenth of the University of Iowa has a number of intuitive Java applets for running power analyses here.

The calculation of statistical power for multiple regression equations featuring categorical moderator variables requires some special considerations, as explained by Aguinis et al. (2005). An online calculator for this sort of analysis can be found at Herman Aguinis’s site at Indiana University here.

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“The primary product of a research inquiry is one or more measures of effect size, not p values.”
~ Jacob Cohen

“Statistical significance is the least interesting thing about the results. You should describe the results in terms of measures of magnitude – not just, does a treatment affect people, but how much does it affect them.”
~ Gene Glass