multiple-testing-correction

Multiple comparison correction methods

509 stars

Best use case

multiple-testing-correction is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Multiple comparison correction methods

Teams using multiple-testing-correction should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/multiple-testing-correction/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/mathematics/skills/multiple-testing-correction/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/multiple-testing-correction/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How multiple-testing-correction Compares

Feature / Agent	multiple-testing-correction	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

Multiple comparison correction methods

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Multiple Testing Correction

## Purpose

Provides multiple comparison correction methods for controlling error rates in simultaneous hypothesis testing.

## Capabilities

- Bonferroni correction
- Holm-Bonferroni method
- Benjamini-Hochberg FDR control
- Sidak correction
- Permutation-based corrections
- Family-wise error rate control

## Usage Guidelines

1. **Error Rate Selection**: Choose FWER vs FDR based on goals
2. **Method Selection**: Apply appropriate correction method
3. **Dependency Handling**: Account for test dependencies
4. **Interpretation**: Report adjusted p-values correctly

## Tools/Libraries

- statsmodels
- scipy.stats
- multcomp (R)
- multtest (R)

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