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scientific-statistical-testing

Identify and quantify statistical differences between groups using standard hypothesis tests.

3|1|Updated Feb 11, 2026
Search Tags:#hypothesis-testing#anova#survival-analysis#statistical-testing#bayesian-inference#enrichment-analysis#multitest-correction
One-click install
npx skills add https://github.com/nahisaho/satori --skill scientific-statistical-testing
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-statistical-testing
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-statistical-testing
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-statistical-testing

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Statistical testing, multiple-testing corrections, and enrichment analyses are essential for drawing reliable conclusions from data but require careful selection of tests and interpretation of results. This skill provides a guided, reproducible pipeline for common hypothesis tests and downstream analytics.

Core Features & Use Cases

  • Supports standard tests such as t-tests, chi-squared tests, ANOVA, Fisher's exact test, and Bayesian inference to assess hypotheses.
  • Includes multiple testing corrections (Bonferroni, Benjamini-Hochberg) and enrichment analyses to control false positives.
  • Use cases include comparing groups in experiments, evaluating pathway enrichment, and applying Bayesian methods to small-sample data.

Quick Start

Run two_group_test on two numeric arrays to compare their distributions.

Frequently Asked Questions about scientific-statistical-testing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I select the right statistical test for comparing multiple groups?▼

To compare multiple groups, ANOVA assesses statistical differences between group means, while chi-squared and Fisher's exact test evaluate categorical data distributions across groups.

What's the best way to control false positives when running multiple hypothesis tests?▼

Control false positives in multiple hypothesis testing by applying correction methods like Bonferroni or Benjamini-Hochberg to adjust p-values and maintain robust significance thresholds.

When should I use Bayesian inference instead of standard t-tests for small-sample data?▼

Use Bayesian inference instead of standard t-tests for small-sample data to quantify statistical patterns and evaluate hypotheses when traditional frequentist assumptions are difficult to meet.

How do I perform enrichment analysis and interpret pathway significance?▼

Perform enrichment analysis to evaluate pathway significance by quantifying statistical patterns in data, reporting effect sizes, and applying multiple testing corrections to ensure reproducible insights.

Does this statistical testing approach report effect sizes alongside p-values?▼

Yes, the statistical testing pipeline ensures reproducibility by reporting both p-values and effect sizes, providing a clear specification of inputs and assumptions for every test.

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