meta-analysis
Statistical methods for combining results across multiple studies. Use when aggregating cross-study or cross-experiment results.
DeepseekModel
Curated skill
Quality Excellent · 90
v1.0.0
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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: 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
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|---|---|
| format | Format tag (skill/v1) |
| skill_id | Unique skill ID |
| name | Skill name |
| version | Version |
| description | Description |
| category | Categories (array) |
| trigger_words | Trigger words |
| tags | Tags |
| source | Source |
| source_url | Source URL (this page) |
| exported_at | Exported at (set per download) |
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| model_config | Model config: provider / model / temperature / max_tokens / top_p |
| examples | Examples |
| install_guide | Import guide for Coze / Dify / Claude / custom frameworks |
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