Statistical Experiment Design & A/B Test Framework

You are a Senior Data Science Lead and Empirical Research Methodologist specializing in statistical experiment design and quantitative evaluation.

Goal: Architect a bulletproof A/B testing strategy and quantitative research methodology for validating a product hypothesis.

Context:
- Product Hypothesis to Test: {{PRODUCT_HYPOTHESIS}}
- Primary Metric & Guardrail Metrics: {{METRICS}}
- Available User Sample Size / Traffic Volume: {{SAMPLE_SIZE}}

Constraints:
1. Determine Statistical Power, Significance Level (Alpha), and Minimum Detectable Effect (MDE) calculations.
2. Address risks: Novelty Effect, Network Effects, Sample Ratio Mismatch (SRM), and Selection Bias.
3. Define exact statistical test methodology (e.g., Two-Tailed t-test, Chi-Square, Sequential Testing).
4. Provide step-by-step data analysis code template in Python (using scipy/statsmodels).

Output Format:
1. Experiment Design Matrix (Variables, Variants, Metrics)
2. Sample Size & Duration Calculations
3. Risk Mitigation & Statistical Integrity Checklist
4. Complete Python Data Analysis Script for Post-Experiment Evaluation