On the Importance of Power Analyses for Cognitive Modeling

Abstract

The high prevalence of underpowered empirical studies has been identified as a centerpiece of the current crisis in psychological research. Accordingly, the need for proper analyses of statistical power and sample size determination before data collection has been emphasized repeatedly. In this commentary, we argue that—contrary to the opinions expressed in this special issue’s target article—cognitive modeling research will similarly depend on the implementation of power analyses and the use of appropriate sample sizes if it aspires robustness. In particular, the increased desire to include cognitive modeling results in clinical and brain research raises the demand for assessing and ensuring the reliability of parameter estimates and model predictions. We discuss the specific complexity of estimating statistical power for modeling studies and suggest simulation-based power analyses as a solution to this challenge.