🚀 A/B Testing Experimentation Framework

Product Analytics | Experimentation | Decision Science

🌐 Live App: https://ab-test-with-stats.onrender.com

🐙 GitHub:AB-Testing-Experimentation-Framework

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📌 Executive Summary

End-to-end experimentation framework enabling product teams to analyze A/B tests, compute statistical significance, and make data-driven decisions.

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🎯 Business Problem

Product teams need reliable experimentation systems to:

• Validate features
• Reduce risk
• Make data-driven decisions

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📈 Key Metrics

• Conversion rate
• p-value
• Confidence interval
• Statistical significance

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🔍 Analysis

• Hypothesis testing
• Power analysis
• Experiment simulation

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💡 Insights

• Statistical significance determines feature success
• Sample size impacts decision reliability

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✅ Recommendations

• Use proper sample size before launching experiments

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🎯 Decision

• Ship / No Ship based on statistical significance

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💰 Business Impact

• Reduces failed feature launches
• Improves product decision accuracy

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🔌 API Preview

Endpoint

POST /api/v1/analyze { "users": 1000, "conversion_rate": 0.12 } { "decision": "Ship", "p_value": 0.03 }

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⚙️ Tools & Tech Stack

Python, FastAPI, Statsmodels

▶️ How to Run

uvicorn main:app --reload

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📁 Project Structure

AB_test_with_stats/ │── main.py │── api/ │── README.md

Author

Denis Agyapong