Z-curve analysis is intended to diagnose the credibility of research results, but its interpretation and statistical properties are often misunderstood. We clarify that the Expected Discovery Rate (EDR; i.e. average observed power) is conceptually distinct from average pre-data power and lacks a clear link to credibility because it reflects both the average pre-data power and the estimated average population effect size. In our review of 37 articles reporting 278 Z-curve applications, 77.3% concluded publication bias, yet 48.2% did not state whether the p-values analyzed reflected focal findings, and 69.1% may have violated the assumption of independent p-values. Simulations further demonstrate that Z-curve estimators can be biased and inconsistent, failing to follow the Law of Large Numbers and potentially producing misleading conclusions. We also question claims made by Soto and Schimmack Credibility of results in emotion science: A Z-curve analysis of results in the journals Cognition & Emotion and Emotion. Cognition and Emotion, (2025), 39(8), 1803-1819, regarding the credibility of emotion-science findings, noting that such conclusions should be interpreted cautiously given the limitations of Z-curve estimates. Overall, we do not recommend using Z-curve to evaluate research findings. Traditional meta-analytic methods remain more appropriate and reliable for statistical conclusions about focal research findings.
Pek et al. (Sat,) studied this question.