Meta-Analysis

A statistical technique that synthesizes results from multiple independent studies to estimate the average effect of an intervention and its variance across contexts. By pooling samples, meta-analysis makes it possible to detect patterns invisible in any single study. Effect sizes—most commonly Cohen’s d or Hedges’ g—serve as the shared currency for combining heterogeneous studies.

A key challenge is publication bias: studies with significant positive results are disproportionately published, inflating pooled effect estimates. Funnel plots and Egger’s test are standard diagnostics. In the context of the replication crisis (Open Science Collaboration, 2015), several high-profile meta-analyses—including the Nudge literature—have shown substantially reduced effect sizes after bias correction.