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A/B Test Analyzer & Power Calculator
Product AnalyticsStatistical toolkit for designing, analyzing, and interpreting A/B tests with confidence.
Overview
💡 Challenge
Product teams run A/B tests but struggle with proper sample size calculation, statistical significance interpretation, and multiple testing corrections.
⚡ Solution
Created a Streamlit app with power calculators, sequential testing support, and automated result interpretation with practical recommendations.
🎯 Impact
Improved experiment quality and reduced false positives through proper statistical methodology.
Technical Details
🛠️ Tech Stack
PythonSciPyStreamlitStatsmodels
✨ Key Features
- ✓Sample size calculator with power analysis
- ✓Statistical significance testing (t-test, chi-square)
- ✓Bayesian A/B test analysis
- ✓Sequential testing support
Key Learnings
- →Proper experimental design prevents invalid conclusions
- →Bayesian methods provide more intuitive probability statements
- →Multiple testing corrections are essential for validity
📊 Data Notes
This project uses synthetic/open data to demonstrate capabilities while maintaining privacy and confidentiality. All methods and approaches are applicable to real-world scenarios.