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---
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
