AI Skill MartBrowse skillsPromptsProgramsLearnWhat is a Skill?
 
← Back to list

Statistical Analysis

An end-to-end guide for research statistics: picking the right test, verifying assumptions, reporting effect sizes, and writing APA-style results.

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

What it does

  • Frames your design (independent vs. paired, number of groups, variable types) and selects the appropriate test — t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian alternatives.
  • Runs automated assumption checks via the bundled scripts/assumption_checks.py: normality (Shapiro-Wilk + Q-Q plots), homogeneity of variance (Levene), outliers, and full OLS diagnostics (VIF, Breusch-Pagan, Durbin-Watson), then recommends remedial tests when assumptions fail.
  • Always pairs p-values with effect sizes and confidence intervals, plus a priori power analysis and post-study sensitivity analysis.
  • Produces APA-formatted write-ups you can drop straight into a manuscript.
  • Enforces statistical integrity rules: no significance shopping, multiple-comparison corrections, honest missing-data handling.

Who it's for

  • Graduate students and researchers writing up quantitative results
  • Analysts of survey or experimental data in psych, medicine, education, UX research
  • Data analysts who want rigorous A/B test reporting
  • Anyone comfortable with basic Python (pandas, scipy)

Examples

  1. "Here are scores for two groups — which test should I use?" → checks normality and variance, runs Welch's t-test, reports Cohen's d with a 95% CI in APA prose.
  2. "How many participants per group do I need to detect d = 0.5?" → solves sample size with statsmodels and states the assumptions behind it.
  3. "Write up my three-condition ANOVA" → partial eta-squared, Tukey HSD post-hocs, and a full paragraph including non-significant comparisons.

· · · Install guide · · ·

Try it now, no install

Paste this into Claude to use the skill without installing anything.

Read the instructions in this file and follow them to help me:
https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/HEAD/skills/statistical-analysis/SKILL.md

What I want: (describe your task here)

If Claude can't open the link, open it yourself and paste the contents instead.

↓ If it works for you, download the ZIP below and install it. Then it runs on its own — no pasting each time.

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 AI 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 repo: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  3. Create the skills folder: mkdir -p ~/.claude/skills
  4. Copy this skill: cp -r scientific-agent-skills/skills/statistical-analysis ~/.claude/skills/
  5. Install the Python stack: 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 ask something like "compare the means of these two groups" — the skill triggers automatically.
View source on GitHub ↗License: MIT