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.
- Version
- 1.2
- License
- MIT
Pinned to revision df088027ff23, so it is the text this page describes rather than whatever the author pushed since.
Files
- skills/statistical-analysis/SKILL.md
- skills/statistical-analysis/references/assumptions_and_diagnostics.md
- skills/statistical-analysis/references/bayesian_statistics.md
- skills/statistical-analysis/references/effect_sizes_and_power.md
- skills/statistical-analysis/references/reporting_standards.md
- skills/statistical-analysis/references/test_selection_guide.md
- skills/statistical-analysis/scripts/assumption_checks.py
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