scientific-statistical-testing

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.