Abstract

An often-cited convention for discovery-oriented behavioral science research states that the general relative seriousness of the antecedently accepted false positive error rate of α = 0.05 be mirrored by a false negative error rate of β = 0.20. In 1965, Jacob Cohen proposed this convention to decrease a β-error rate typically in vast excess of 0.20. Thereby, we argue, Cohen (unintentionally) contributed to the wide acceptance of strongly uneven error rates in behavioral science. Although Cohen’s convention can appear epistemically reasonable for an individual researcher, the comparatively low probability that published effect size estimates are replicable renders his convention unreasonable for an entire scientific field. Appreciating Cohen’s convention helps to understand why even error rates (α = β) are “non-conventional” in behavioral science today, and why Cohen’s explanatory reason for β = 0.20—that resource restrictions keep from collecting larger samples—can easily be mistaken for the justificatory reason it is not.