A Response to Pek et al.’s Commentary on Z-Curve: Clarifying the Assumptions of Selection Models

Pek et al. (2026) comment on Soto and Schimmack (2025) and raise concerns about the use of z-curve to evaluate the credibility of emotion research. Their central criticism is based on simulations showing that z-curve can overestimate power when selection operates not only at the level of statistical significance but also within the set of significant results as a function of effect size. This point is correct: if researchers selectively publish larger significant effects while suppressing smaller significant ones, selection models that assume threshold-based filtering can be biased. However, this limitation is not unique to z-curve and applies equally to other selection models used in meta-analysis. More importantly, there is currently little empirical evidence that researchers systematically engage in effect-size-based selection among statistically significant focal tests. In contrast, extensive evidence indicates that high success rates in psychology are primarily driven by selection for significance and p-hacking. Under these more realistic conditions, z-curve provides informative estimates of average power and selection bias. Our results also demonstrate substantial inflation of effect size estimates in traditional meta-analyses that ignore selection processes. For these reasons, we reject the recommendation to rely solely on standard meta-analytic approaches and instead advocate the use of selection models to obtain more realistic estimates of effect sizes in emotion research.