statistical-analysis
Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required sample sizes, or write up results - even if they never name a specific test. Covers t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian methods. For low-level model APIs, see the statsmodels and pymc skills.
npx skills add K-Dense-AI/scientific-agent-skills --skill statistical-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Guides the user through planning and executing statistical analyses from framing the question to reporting results, covering test selection, assumption checks, effect sizes, and APA-formatted reporting.
How it works
- Establishes a framed question before touching data, declaring hypothesis, outcome, predictor variables, and design.
- Inspects data per group (n, mean, SD, median, missing values) and requires plotting raw data before testing.
- Recommends a test using a quick reference; directs to a broader guide for designs beyond basics.
- Checks assumptions with scripts/assumption_checks.py; if violated, switches to remedial tests and reports both plan and change.
- Runs the test and computes effect size alongside it.
- Reports results using APA templates, including descriptives, exact statistics, effect sizes with CIs, and checks performed.
- Provides concrete code examples for common tests (t-test, ANOVA, regression) and Bayesian analyses, with explicit commands and libraries (e.g., pingouin, pymc, arviz).
When to use it
- When conducting statistical hypothesis tests (t-tests, ANOVA, chi-square, non-parametric)
- When performing regression or correlation analyses
- When running Bayesian statistical analyses
- When checking statistical assumptions and diagnostics
- When calculating effect sizes and conducting power analyses
- When reporting statistical results in APA format
- When analyzing experimental or observational data for research
What it can touch
- Primary libraries: pingouin, scipy.stats, statsmodels, pymc, arviz
- Uses:
scripts/assumption_checks.py, and functions likecheck_normality,check_homogeneity_of_variance,check_regression_diagnostics(as shown in examples)
Caveats
- Requires installation steps that pin versions (e.g., pingouin>=0.6, pymc>=5.0, arviz>=1.0).
- Notes compatibility specifics for outputs (e.g., Pingouin column names like
p_val,CI95, etc.). - Emphasizes reporting and interpretation aligned with APA standards and cautions against post-hoc power misinterpretations.
# 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. The goal is an analysis a reviewer could not tear apart: the right test, verified assumptions, honest effect sizes, and a complete write-up. ## 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 Use **uv** to install the libraries used in this skill. Pin versions in production; unpinned installs are fine for exploration. ```bash # Core frequentist stack (Python 3.10+; 3.12+ recommended for latest SciPy/ArviZ) uv pip install "pingouin>=0.6" "scipy>=1.11" "statsmodels>=0.14.6" pandas matplotlib seaborn # Bayesian modeling (PyMC 5 + ArviZ) uv pip install "pymc>=5.0" "arviz>=1.0" ``
- Overview
- When to Use This Skill
- Installation
- Analysis Workflow
- Test Selection Guide
- Quick Reference: Choosing the Right Test
- Assumption Checking
- What to Do When Assumptions Are Violated
- Running Statistical Tests
- T-Test with Complete Reporting
- ANOVA with Post-Hoc Tests
- Linear Regression with Diagnostics
- Bayesian T-Test
- Effect Sizes
Core frequentist stack (Python 3.10+; 3.12+ recommended for latest SciPy/ArviZ) uv pip install "pingouin>=0.6" "scipy>=1.11" "statsmodels>=0.14.6" pandas matplotlib seaborn Bayesian modeling (PyMC 5 + ArviZ) uv pip install "pymc>=5.0" "arviz>=1.0"
What does the statistical-analysis skill do?
Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required sample sizes, or write up results - even if they never name a specific test. Covers t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian methods. For low-level model APIs, see the statsmodels and pymc skills.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill statistical-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.