Abstract

Objective:  To describe the concept of statistical power as related to comparative interventions and how various factors, including sample size, affect statistical power.
Background:  Having a sufficiently sized sample for a study is necessary for an investigation to demonstrate that an effective treatment is statistically superior. Many researchers fail to conduct and report a priori sample-size estimates, which then makes it difficult to interpret nonsignificant results and causes the clinician to question the planning of the research design.
Description:  Statistical power is the probability of statistically detecting a treatment effect when one truly exists. The α level, a measure of differences between groups, the variability of the data, and the sample size all affect statistical power.
Recommendations:  Authors should conduct and provide the results of a priori sample-size estimations in the literature. This will assist clinicians in determining whether the lack of a statistically significant treatment effect is due to an underpowered study or to a treatment's actually having no effect.