AB Testing for Data Scientists & Product Managers

Use a structured hypothesis framework to define measurable, decision-driving experiment statements.

Select primary, secondary, and guardrail metrics that map directly to product goals.

Practice evaluating metric trade-offs using real feature scenarios.

Interpret confidence intervals, lift, and directional signals to assess impact.

Compare variants using practical heuristics that guide launch/no-launch recommendations.

Work through examples where statistical significance doesn’t match business significance.

Break down experimentation patterns used at companies like Uber and Spotify.

Analyze how these companies chose hypotheses, metrics, and rollout strategies.

Map each case to your own product context to understand transferability.

Create a concise decision brief summarizing problem, hypothesis, metrics, results, and recommendation.

Practice translating statistical output into business-relevant language for PMs and engineering.

Use templates that make stakeholders act on insights faster.

Evaluate the strength of hypotheses, metric selection, sample sizing, and operational setup.

Identify misaligned metrics and propose fixes grounded in product strategy.

Redesign weak experiments during live reviews and group exercises.

Learn to spot flawed experiment setups before they waste time or traffic.

Build habits for faster, more rigorous decision-making rooted in product intent.

Benchmark your experimentation skills with real-world scenarios used in senior DS interviews.