Statistical Analysis
Handles the full academic statistical workflow from test selection through APA-formatted reporting. You get guided help choosing between t-tests, ANOVA, chi-square, regression, and their non-parametric alternatives based on your data characteristics. The built-in assumption checking module runs Shapiro-Wilk, Levene's test, and outlier detection with diagnostic plots before you commit to a test. Uses pingouin for clean effect sizes and confidence intervals, with statsmodels and PyMC backing more complex models. The documentation is unusually thorough with decision trees and compatibility notes for the 2025-2026 scipy/pingouin ecosystem changes. Best for research contexts where you need defensible test choices and proper reporting, not for production model deployment.
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- 7
- skills.sh installs
- 2.4K
- GitHub stars
- 56.6K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add k-dense-ai/scientific-agent-skills --skill statistical-analysis --agent claude-codeInstalls into .claude/skills of the current project.
Files
Statistical Analysis
Overview
Conduct hypothesis tests (t-tests, ANOVA, chi-square), regression, correlation, and Bayesian analyses with systematic assumption checking, effect sizes, and APA-style reporting. Define the estimand and sampling units, diagnose model limitations, and report effect estimates with uncertainty. Numerical screens cannot certify assumptions or scientific validity.
When to Use This Skill
Use this skill when:
- Conducting statistical hypothesis tests (t-tests, ANOVA, chi-square, non-parametric)
- Performing regression or correlation analyses
- Running Bayesian statistical analyses
- Checking statistical assumptions and diagnostics
- Calculating effect sizes and conducting power analyses
- Reporting statistical results in APA format
- Analyzing experimental or observational data for research
Installation
Targets Pingouin 0.7.0, SciPy 1.18.1, statsmodels 0.15.0, PyMC 6.3.2 and ArviZ 1.3.0 (reviewed 2026-10-01). Create a separate environment; do not add mutually incompatible scientific stacks to a shared project environment.
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- Repository
- github.com/k-dense-ai/scientific-agent-skills
- Categories
- Testing & QA, AI & Agent Building, Data Science & ML
- First seen
- Jun 3, 2026
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