Statistical power analysis can improve fisheries research and management

Abstract

Past statistical power analyses show that abundance estimation techniques usually have high β, the probability of not rejecting a null hypothesis when it should have been, and that only large effects are detectable. I review relationships among β, power, detectable effect size, sample size, and sampling variability. I show how statistical power analysis can help interpret past results and improve designs of future experiments, impact assessments, and management regulations. I make recommendations for researchers and decision makers, including routine application of power analysis, more cautious management, and reversal of the burden of proof to put it on industry, not management agencies. -from Author