"Can you walk me through how you would design and analyze an A/B test?"
A/B testing is the gold standard for data-driven decision making at tech companies. Every data analyst, product manager, and growth engineer role requires A/B testing knowledge. This question tests your understanding of experimental design, statistical reasoning, and practical business application.
Define the hypothesis: clearly state the null hypothesis (H0: no difference) and alternative hypothesis (H1: treatment is better/different). Define the primary metric (e.g., conversion rate, revenue per user).
Design the experiment: determine the randomization unit (user, session, device), ensure proper random assignment, and decide on control/treatment split (typically 50/50).
Calculate sample size: use power analysis based on desired significance level (alpha, typically 0.05), power (typically 0.80), and minimum detectable effect (MDE). Larger effects need fewer samples.
Run the test: ensure adequate duration (at least 1-2 full business cycles), watch for novelty effects, and avoid peeking at results before reaching sample size.
Analyze results: calculate the test statistic, p-value, and confidence interval. Consider practical significance alongside statistical significance. Check for Simpson's paradox and segment-level effects.
A/B testing is a controlled experiment comparing two versions to determine which performs better on a defined metric. Here is my approach: First, I define clear hypotheses. For example, H0: changing the CTA button color has no effect on conversion rate; H1: the new color increases conversion rate. I identify the primary metric (conversion rate), guardrail metrics (page load time, bounce rate), and the minimum detectable effect — say, a 2% relative improvement from a 10% baseline to 10.2%. Next, I calculate sample size using power analysis. With alpha=0.05, power=0.80, and a 2% relative MDE, I might need ~150,000 users per group. I divide this by daily traffic to estimate test duration, ensuring it covers at least one full week to account for day-of-week effects. During the test, proper randomization is critical — I use user-level randomization with consistent bucketing (hash of user ID) so users always see the same variant. I avoid peeking at results or stopping early, which inflates false positive rates. After reaching the required sample size and duration, I analyze results using a two-proportion z-test (for conversion rates) or t-test (for continuous metrics). I calculate the p-value, confidence interval, and effect size. If p < 0.05, I reject the null hypothesis, but I also check practical significance — a statistically significant 0.01% improvement may not be worth the engineering cost. I segment results by platform, geography, and user cohort to check for heterogeneous treatment effects. Finally, I document the decision, rationale, and any learnings for the team.
Always mention both statistical significance AND practical significance — interviewers want to see business judgment.
Know why you should not peek at results — it inflates Type I error rates (multiple testing problem).
Be ready to discuss alternatives when A/B testing is not possible (quasi-experiments, difference-in-differences).
Use HireMe AI during your interview to recall statistical formulas and common pitfalls.
Options: increase test duration, focus on a metric with higher variance or higher baseline rate, use a larger minimum detectable effect, or consider Bayesian methods which can work with smaller samples.
Checking results before reaching target sample size and stopping when you see significance inflates false positive rates far beyond 5%. Solutions: pre-commit to sample size, use sequential testing methods, or use always-valid confidence intervals.
When testing multiple variants or metrics, use Bonferroni correction (divide alpha by number of tests) or control the False Discovery Rate. Otherwise, with 20 metrics at alpha=0.05, you expect 1 false positive by chance.
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