# Meta Analysis

Statistical methods for combining results across multiple studies.

- Repository: https://github.com/aiming-lab/AutoResearchClaw (14798 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-08-19
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add aiming-lab/AutoResearchClaw --skill meta-analysis -g -a claude-code -y`
Codex: `npx skills add aiming-lab/AutoResearchClaw --skill meta-analysis -g -a codex -y`
Cursor: `npx skills add aiming-lab/AutoResearchClaw --skill meta-analysis -g -a cursor -y`
Gemini CLI: `npx skills add aiming-lab/AutoResearchClaw --skill meta-analysis -g -a gemini-cli -y`

## Description

Statistical methods for combining results across multiple studies. Use when aggregating cross-study or cross-experiment results.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## 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