The testing of research hypotheses is directly comparable to the dichotomous decision-making of medical diagnosis or jury trials--not ill/ill, or innocent/guilty decisions. There are costs in both kinds of error, type I errors of falsely rejecting a null hypothesis or type II errors of falsely rejecting an alternative hypothesis. It is important to consider the power of a statistical test (that is, the likelihood of avoiding type II errors) as well as the significance level (the likelihood of avoiding type I errors) before an experiment is begun. Techniques for estimating the power of a statistical test for a particular experimental design are presented. (CTM)