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Statistical Analysis

A guided statistics workflow that picks the right test, verifies assumptions, reports effect sizes, and writes APA-formatted results.

Data & AnalyticsIntermediate★ 33,030⑂ 3,248AI score 9/10Last updated: Aug 9, 2026

What it does

Walks you through a defensible statistical analysis of research, experimental, or survey data.

  • Test selection: t-tests, ANOVA, chi-square/Fisher, Mann-Whitney, Kruskal-Wallis, Friedman, correlation, linear/logistic regression
  • Assumption checking via the bundled scripts/assumption_checks.py: normality (Shapiro-Wilk + Q-Q), homogeneity of variance (Levene), outliers, and full OLS diagnostics (Breusch-Pagan, Durbin-Watson, VIF)
  • Effect sizes & power: Cohen's d, partial η², Cramér's V with confidence intervals, a priori sample-size calculation, and sensitivity analysis instead of misleading post-hoc power
  • Bayesian alternatives with PyMC/ArviZ: posteriors, credible intervals, and direct probability statements for one-sided questions
  • APA reporting templates for t-tests, ANOVA, multiple regression, and Bayesian analyses
  • Statistical integrity checklist: confirmatory vs. exploratory, multiple-comparison correction, missing data, reproducibility

Who it's for

  • Grad students and researchers who need a stats section reviewers can't tear apart
  • Analysts migrating from SPSS/JASP to Python (pingouin, statsmodels, PyMC)
  • Data analysts and UX researchers interpreting A/B tests or survey results
  • Anyone planning sample size before collecting data

Example uses

  1. Two-group comparison: "Here's a CSV of treatment vs. control scores." → descriptives and plots → normality/variance checks → Welch's t-test → Cohen's d with CI → ready-to-paste APA sentence
  2. Sample-size planning: "Three conditions, medium effect, 80% power — how many participants?" → FTestAnovaPower returns total N and per-group n
  3. Regression diagnostics: "My OLS residuals look off." → 4-panel residual plots, VIF, Breusch-Pagan; if heteroscedastic, re-report with HC3 robust standard errors

· · · Install guide · · ·

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude Skill Mart.
Copy the skills/statistical-analysis folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/statistical-analysis/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills && mkdir -p ~/.claude/skills && cp -r /tmp/scientific-agent-skills/skills/statistical-analysis ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Open a terminal.
  2. Clone the repository: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  3. Create the skills folder if needed: mkdir -p ~/.claude/skills
  4. Copy just this skill: cp -r scientific-agent-skills/skills/statistical-analysis ~/.claude/skills/
  5. Install the Python libraries: uv pip install "pingouin>=0.6" "scipy>=1.11" "statsmodels>=0.14.6" pandas matplotlib seaborn (add uv pip install "pymc>=5.0" "arviz>=1.0" for Bayesian work)
  6. Restart Claude Code and try a prompt like "Compare test scores between these two groups."
View source on GitHub ↗License: MIT