Statistical methods for combining results across multiple studies. Use when aggregating cross-study or cross-experiment results.
name meta-analysis description Statistical methods for combining results across multiple studies. Use when aggregating cross-study or cross-experiment results. metadata {"category":"experiment","trigger-keywords":"meta-analysis,effect size,pooled,cross-study,aggregat","applicable-stages":"7,14","priority":"5","version":"1.0","author":"researchclaw","references":"Borenstein et al., Introduction to Meta-Analysis, 2009"} Meta-Analysis Best Practice When comparing results across studies or experiments: Report effect sizes, not just p-values Use standardized metrics for cross-study comparison Account for heterogeneity (different setups, datasets, seeds) Report confidence intervals alongside point estimates Use forest plots to visualize cross-study comparisons Identify and discuss outliers or inconsistent results Consider publication bias when interpreting aggregate results