Pek et al. (2026. What does a Z-curve analysis tell us? , 1-16) comment on Soto and Schimmack (2025. Credibility of results in emotion science: A z-curve analysis of results in the journals Cognition & Emotion and Emotion. ) 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 the expected discovery rate 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 for effect-size bias, while there is ample evidence of selection based on significance. Under these more realistic conditions, z-curve provides informative estimates of (a) selection bias, (b) the expected replication rate, and (c) the false positive risk. 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 advocate for the use of selection models to obtain more realistic estimates.