crucible_bench

# Changelog

All notable changes to this project will be documented in this file.

## [0.2.0] - 2025-11-24

### Added
- Complete statistical testing framework with parametric (Student's, Welch's, paired t-tests, one-way ANOVA) and non-parametric (Mann-Whitney U, Wilcoxon signed-rank, Kruskal-Wallis) coverage using accurate distribution functions
- Expanded effect size suite including Cohen's d, Hedges' g, Glass's delta, paired Cohen's d, eta-squared/omega-squared, and rank-biserial correlation with interpretation guidance
- Confidence intervals (analytical and bootstrap) and power analysis (a priori and post-hoc for t-tests and ANOVA) with actionable recommendations
- High-level analysis helpers for automatic test selection plus experiment DSL for A/B tests, ablation studies, and hyperparameter sweeps
- Publication-ready exports to Markdown, LaTeX, and HTML with standardized result metadata

## [0.1.0] - 2025-10-07

### Added
- Initial release
- Comprehensive statistical testing framework for AI/ML research
- Parametric tests (t-tests, ANOVA) and non-parametric tests (Mann-Whitney, Wilcoxon, Kruskal-Wallis)
- Effect size measures (Cohen's d, Hedges' g, Glass's delta, eta-squared, omega-squared)
- Power analysis with a priori and post-hoc calculations
- Confidence intervals using bootstrap and analytical methods
- High-level experiment DSL for A/B tests, ablation studies, and hyperparameter sweeps
- Publication-ready export formats (Markdown, LaTeX, HTML)

### Documentation
- Comprehensive README with examples
- API documentation for all statistical tests
- Usage examples for common AI research scenarios
- Best practices guide for statistical rigor in AI research