Gluth*, S, & Jarecki, JB (2019). On the Importance of Power Analyses for Cognitive Modeling. Computational Brain & Behavior 2, 266–270
Data scientist in Basel
portfolio_page-template-default,single,single-portfolio_page,postid-2761,bridge-core-3.1.8,qode-page-transition-enabled,ajax_fade,page_not_loaded,,qode_grid_1300,side_area_uncovered_from_content,overlapping_content,qode-content-sidebar-responsive,qode-theme-ver-30.5,qode-theme-bridge,disabled_footer_top,qode_header_in_grid,qode-portfolio-single-template-5,wpb-js-composer js-comp-ver-7.6,vc_responsive

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


Upon request


Online codebook


Sebastian Gluth

Cognitive modeling, Publication