What does a <i>Z</i> -curve analysis tell us?

<i>Z</i>-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 <i>Z</i>-curve applications, 77.3% concluded publication bias, yet 48.2% did not state whether the <i>p</i>-values analyzed reflected focal findings, and 69.1% may have violated the assumption of independent <i>p</i>-values. Simulations further demonstrate that <i>Z</i>-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 <i>Z</i>-curve analysis of results in the journals <i>Cognition & Emotion</i> and <i>Emotion</i>. <i>Cognition and Emotion</i>, (2025), <i>39</i>(8), 1803-1819], regarding the credibility of emotion-science findings, noting that such conclusions should be interpreted cautiously given the limitations of <i>Z</i>-curve estimates. Overall, we do not recommend using <i>Z</i>-curve to evaluate research findings. Traditional meta-analytic methods remain more appropriate and reliable for statistical conclusions about focal research findings.