Power Analysis

Traditionally, data collected in a research study is submitted to a significance test to assess the viability of the null hypothesis. The p-value provided by the significance test, and used to reject the null hypothesis, is a function of three factors: The larger the observed effect, the larger the sample size, and/or the more liberal the criterion required for significance (alpha ), the more likely it is that the test will yield a significant p-value. A power analysis, executed when the study is being planned, is used to anticipate the likelihood that the study will yield a significant effect and is based on the same factors as the significance test itself. Specifically, the larger the effect size used in the power analysis, the larger the sample size, and/or the more liberal the criterion required for significance (alpha), the higher the expectation that the study will yield a statistically significant effect. These three factors, together with power, form a closed system - once any three are established, the fourth is completely determined. The goal of a power analysis is to find an appropriate balance among these factors by taking into account the substantive goals of the study, and the resources available to the researcher.