Walk through an A/B test end-to-end

Describe the process you would follow to design, run, and analyze an A/B test for a product change. Your response should cover:

• Framing the hypothesis and selecting primary, diagnostic, and guardrail metrics.
• Experiment design: randomization unit, population, exposure definition, duration, and how to account for novelty/seasonality.
• How to compute sample size / MDE / power.
• Data quality checks (such as SRM), logging problems, and how to confirm randomization.
• Statistical analysis methods (confidence intervals, p-values, multiple testing, sequential peeking).
• How to interpret outcomes and make a launch decision, including practical versus statistical significance.
• Common pitfalls (such as interference/network effects, noncompliance, missing data).

Overview: This question tests a data scientist's skills in experimental design, statistical inference, causal reasoning, metric selection, and data quality assurance for A/B testing.