What does a Z-curve analysis tell us?

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.