For genetic studies, statistically significant variants explain far less trait variance than sub-threshold association signals. To dimension follow-up studies, researchers need to accurately estimate true effect sizes at each SNP, e.g. the true mean of odds ratios (ORs)/regression coefficients (RRs) or Z-score noncentralities. Naïve estimates of effect sizes incur winners curse biases, which are reduced only by laborious winners curse adjustments (WCAs). Given that Z-scores estimates can be theoretically translated on other scales, we propose a simple method to compute WCA for Z-scores, i.e. their true means/noncentralities.