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

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

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

Q: How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

Question

Walk through how you would design, run, and analyze an A/B test for a product change.

Your answer should include:

Hypothesis framing and choosing primary , diagnostic , and guardrail metrics.
Experiment design: unit of randomization, population, exposure definition, duration, and handling novelty/seasonality.
How you determine sample size / MDE / power .
Data quality checks (e.g., SRM), logging issues, and how you validate randomization.
Statistical analysis approach (confidence intervals, p-values, multiple testing, sequential peeking).
How you interpret results and make a launch decision, including practical vs statistical significance.
Common pitfalls (e.g., interference/network effects, noncompliance, missing data).

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

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

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