A/B Testing Analysis Framework 🧪

A comprehensive statistical framework for analyzing A/B tests with both frequentist and Bayesian approaches. Make data-driven decisions with confidence.

🎯 Project Overview

This framework provides end-to-end A/B testing capabilities: from experimental design (sample size calculation) to post-test analysis (statistical significance, business impact). Built for data scientists who need rigorous statistical methods combined with intuitive visualizations.

✨ Key Features

1. Statistical Analysis

• Two-proportion Z-test with p-values
• 95% confidence intervals
• Effect size calculations
• Multiple testing correction awareness

2. Bayesian Analysis

• Posterior probability distributions
• P(Treatment > Control) calculation
• Credible intervals
• Monte Carlo simulations (100K iterations)

3. Power Analysis

• Pre-test sample size calculator
• Statistical power estimation
• Minimum Detectable Effect (MDE) analysis
• Test duration recommendations

4. Business Impact

• Revenue impact projections
• ROI calculations
• Risk assessment
• Actionable recommendations

5. Interactive Dashboard

• Real-time analysis
• Multiple visualization tabs
• Test simulation mode
• Sample size calculator

🛠️ Technologies Used

• Python 3.8+
• Streamlit: Interactive web framework
• Scipy & Statsmodels: Statistical testing
• Plotly: Interactive visualizations
• NumPy & Pandas: Data manipulation
• Jupyter Notebook: Analysis documentation

📁 Project Structure

ab-testing-framework/
│
├── data/
│   └── ab_test_data.csv          # Synthetic test data
│
├── notebooks/
│   └── 01_ab_testing_analysis.ipynb  # Complete analysis
│
├── models/
│   ├── test_results.json         # Saved analysis results
│   └── ab_test_report.png        # Summary report
│
├── app.py                         # Streamlit dashboard
├── requirements.txt
├── README.md
└── .gitignore

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• pip package manager

Installation

1. Clone the repository

git clone https://github.com/Emart29/ab-testing-framework.git
cd ab-testing-framework

1. Install dependencies

pip install -r requirements.txt

1. Run the application

streamlit run app.py

1. Open your browser and navigate to http://localhost:8501

📊 Example Results

Test Scenario

• Control Group: 10,000 users, 11.45% conversion
• Treatment Group: 10,000 users, 14.09% conversion
• Absolute Lift: 2.64 percentage points
• Relative Lift: 23.06%

Statistical Results

• P-value: < 0.000001 (highly significant)
• Z-statistic: 5.59
• Confidence Interval: [1.72%, 3.56%]
• Bayesian P(B>A): 100.0%

Business Impact

• Revenue Lift per User: $3.27
• Annual Revenue Impact: $3.9M (for 100K monthly users)
• Recommendation: ✅ Launch Treatment

🔬 Statistical Methodology

Frequentist Approach

1. Hypothesis Testing

   • H₀: p_treatment ≤ p_control
   • H₁: p_treatment > p_control
   • Significance level: α = 0.05

2. Two-Proportion Z-Test

   • Tests equality of proportions
   • Calculates exact p-values
   • Provides confidence intervals

Bayesian Approach

1. Prior Distribution: Uniform Beta(1,1)
2. Posterior: Beta(successes + 1, failures + 1)
3. Monte Carlo: 100,000 simulations
4. Output: Direct probability statements

Power Analysis

• Cohen's h: Effect size for proportions
• Power: 80% (industry standard)
• Accounts for: Type I and Type II errors

💡 Use Cases

1. E-commerce: Test new checkout flows
2. SaaS Products: Compare onboarding experiences
3. Marketing: Evaluate campaign effectiveness
4. Product Features: Validate new features
5. Pricing: Test pricing strategies

📈 Key Insights Demonstrated

Statistical Rigor

• Proper hypothesis testing
• Multiple approaches (frequentist + Bayesian)
• Power analysis for experimental design
• Business impact quantification

Data Science Skills

• Statistical inference
• Experimental design
• Causal reasoning
• Business translation

🎓 Learning Outcomes

• A/B test design and analysis
• Frequentist vs Bayesian statistics
• Sample size calculation
• Statistical power concepts
• Business metrics translation
• Interactive dashboard development

🔮 Future Enhancements

• Sequential testing (early stopping)
• Multi-armed bandit algorithms
• CUPED variance reduction
• Stratified analysis
• Multiple metric tracking
• Automated monitoring and alerts
• Integration with analytics platforms

📚 Resources & References

• Statistical Methods: Two-proportion Z-test, Beta-Binomial conjugacy
• Sample Size: GPower methodology
• Best Practices: Kohavi, Tang & Xu (Trustworthy Online Controlled Experiments)

⚠️ Important Notes

When to Use This Framework

• ✅ Conversion rate optimization
• ✅ Click-through rate testing
• ✅ Binary outcome metrics
• ✅ Independent user assignment

Limitations

• Assumes independent observations
• Binary outcomes only (extend for continuous)
• No correction for multiple testing (implement Bonferroni/FDR if needed)
• Requires proper randomization

🎯 Why This Matters for Data Scientists

A/B testing is the gold standard for causal inference in tech companies. This project demonstrates:

1. Statistical Maturity: Understanding both frequentist and Bayesian approaches
2. Practical Application: Sample size calculations prevent underpowered tests
3. Business Acumen: Translating statistics into revenue impact
4. Communication: Interactive dashboards for stakeholders

Most ML models are descriptive. A/B testing is prescriptive. This shows understanding on how to make causal claims and drive business decisions.

👤 Author

[Your Name]

• LinkedIn: Emmanuel NWanguma
• GitHub: Emart29
• Email: nwangumaemmanuel29@gmail.com

📝 License

This project is licensed under the MIT License.

🙏 Acknowledgments

• Statistical methodology based on industry best practices
• Inspired by experimentation platforms at major tech companies
• Built as part of a comprehensive data science portfolio

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⭐ If this helped you understand A/B testing, please star the repo!

💬 Questions? Open an issue or reach out on LinkedIn