---
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:
1. Report effect sizes, not just p-values
2. Use standardized metrics for cross-study comparison
3. Account for heterogeneity (different setups, datasets, seeds)
4. Report confidence intervals alongside point estimates
5. Use forest plots to visualize cross-study comparisons
6. Identify and discuss outliers or inconsistent results
7. Consider publication bias when interpreting aggregate results