📊 Data Analysis — A/B Testing Statistical Framework Demo

• Sample Size Calculator — Power analysis for proportions and continuous metrics
• Frequentist Tests — Z-test for proportions, Welch's t-test, chi-square goodness-of-fit
• Sequential Testing — O'Brien-Fleming spending function for early stopping
• Bayesian A/B — Beta-Binomial model with posterior probability of winning
• CUPED Variance Reduction — Pre-experiment covariate adjustment to reduce required sample size
• Results Reporter — Generate structured experiment summaries with confidence intervals

pip install -r requirements.txt
python -m src.abtest.main

src/abtest/
├── __init__.py           # Package exports
├── main.py               # Demo runner
├── sample_size.py        # Power analysis and sample size calculation
├── frequentist.py        # Z-test, t-test, chi-square implementations
├── sequential.py         # Group sequential testing with alpha spending
├── bayesian.py           # Beta-Binomial Bayesian A/B testing
├── cuped.py              # CUPED variance reduction
├── reporter.py           # Structured results reporting
tests/
├── test_sample_size.py   # Validates power calculations against known values
├── test_frequentist.py   # Validates test statistics
├── test_bayesian.py      # Validates posterior calculations
configs/
├── experiment_config.yaml  # Example experiment configuration
guides/
├── experiment_design.md    # How to design a rigorous experiment
├── interpreting_results.md # How to read and act on test results