Sensitivity Analysis

About

Ensures the integrity of scientific and data-driven research by systematically testing the stability of results against different methodological choices. By facilitating exclusion analyses, alternative statistical modeling, and missing data simulations, this skill helps identify whether findings are truly robust or merely artifacts of specific decisions. It is an essential tool for automated research workflows, providing clear guidance on interpreting consistent results versus those that require caution due to sensitivity.

Key Features

• Exclusion analysis for outliers and high risk-of-bias studies
• 2 GitHub stars
• Measurement sensitivity via alternative scoring and outcome definitions
• Comparison of parametric and non-parametric statistical tests
• Standardized interpretation framework for reporting confidence and uncertainty
• Missing data handling including multiple imputation and best/worst-case scenarios

Use Cases

• Testing if results remain significant when accounting for missing data via imputation
• Determining if research conclusions are sensitive to specific statistical transformations
• Validating meta-analysis findings by performing one-study-removed analyses