How credible are published significant results: estimating average true power with P-Curve or Z-curve?

Concerns about publication bias and the replicability of psychological research have motivated the development of methods that evaluate the credibility of published significant results. Two prominent approaches are p-curve and z-curve, which use the distribution of significant p-values to estimate the evidential value of a set of studies. Although both methods rely on selection models, they differ critically in how they handle heterogeneity in true effect sizes and statistical power. We systematically vary heterogeneity in effect sizes (τ) across a realistic range and show that both methods perform well when heterogeneity is small, but only z-curve performs well when heterogeneity is moderate. We illustrate the practical consequences using data from the Reproducibility Project, showing that only z-curve correctly predicts that many replication attempts will fail. Because z-curve performs as well as or better than p-curve across conditions, we recommend using z-curve to evaluate the credibility and evidential value of meta-analytic findings, particularly when meta-analyses synthesize conceptual rather than direct replications.