When Averaging Goes Wrong: The Case for Mixture Model Estimation in Psychological Science

Recent failed attempts to replicate numerous psychological findings have raised concerns about methodological practices. Here, we argue that when effects come from multiple underlying distributions, meta-analytic averages can be deceptive. The underlying distribution properties, we propose, should be modeled, based on the variability in a population of effect sizes.