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sensitivity-analysis

@astoreyai/ai_scientist
2
0

Conduct sensitivity analyses to test robustness of findings. Use when: (1) Testing assumption violations, (2) Meta-analysis robustness, (3) Handling missing data, (4) Examining outliers.

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SKILL.md

name sensitivity-analysis
description Conduct sensitivity analyses to test robustness of findings. Use when: (1) Testing assumption violations, (2) Meta-analysis robustness, (3) Handling missing data, (4) Examining outliers.
allowed-tools Read, Write, Bash
version 1.0.0

Sensitivity Analysis Skill

Purpose

Test whether findings are robust to analytical decisions and assumptions.

Types of Sensitivity Analyses

1. Exclusion Analyses

  • Remove outliers
  • Remove high risk-of-bias studies
  • One-study-removed analysis

2. Analytical Decisions

  • Different statistical tests
  • Parametric vs non-parametric
  • Different transformations

3. Missing Data

  • Complete case analysis
  • Best-case scenario
  • Worst-case scenario
  • Multiple imputation

4. Measurement

  • Different outcome definitions
  • Different time points
  • Alternative scoring methods

Interpretation

Robust Findings:

  • Results consistent across analyses
  • Conclusions unchanged
  • High confidence

Sensitive Findings:

  • Results vary by decision
  • Interpret with caution
  • Report uncertainty

Example

"Results were robust to removal of the highest risk-of-bias study (d=0.48 vs d=0.52) and remained significant when using non-parametric tests (p=.002)."

Version: 1.0.0