Statistically significant results from low-power analyses: A comedy of errors.

ABSTRACT:

Background

When low-power analyses yield statistically significant results, they likely overestimate the true effect. Although sample estimates are symmetrically distributed around the true value, those that are by chance very high are more likely to achieve statistical significance. The bias induced by the significance filter increases as power decreases. Here we sought to quantify the estimation bias associated with low power and to contrast it with the type M error, which assesses the same phenomenon from a different perspective.

Methods

We used simulations to quantify estimation bias in relation to power among statistically significant results. We computed the type M error, relative bias (ratio of the estimated mean differences and the true value), and proportions of res

SUBMITTER: Jaksic C 

PROVIDER: S-EPMC12856849 | biostudies-literature | 2026 Jun

REPOSITORIES: biostudies-literature