Experimentation and Product Validation Questions
Designing and interpreting experiments and validation strategies to test product hypotheses. Includes hypothesis formulation, experimental design, sample sizing considerations, metrics selection, interpreting results and statistical uncertainty, and avoiding common pitfalls such as peeking and multiple hypothesis testing. Also covers qualitative validation methods such as interviews and pilots, and using a mix of methods to validate product ideas before scaling.
HardSystem Design
0 practiced
Design an internal experimentation platform that supports feature flagging, deterministic randomization, metric computation, experiment registry, and audit logging for 500M monthly users. Outline components (assignment service, event ingestion, metrics store), data schema considerations for assignment enrichment, guarantees needed (consistency, idempotency), and how to support common experiment types: A/A tests, holdouts, and ramping.
HardTechnical
0 practiced
Given a daily aggregated table daily_results(date, bucket, users, conversions) for an experiment, write a Postgres SQL query that computes cumulative conversion rates per day for control and treatment, the cumulative absolute lift, the z-test p-value for the cumulative difference per day, and flags the first date where p-value < 0.05. Note: this replicates naive sequential peeking; explain its pitfalls in the comments.
EasyTechnical
0 practiced
Explain what an A/B test is and define the following terms in the context of product experimentation: null hypothesis, treatment, control, p-value, confidence interval, and statistical power. Provide a concise product example (2–3 sentences) of a hypothesis that would be suitable for an A/B test and state which primary metric you would choose and why.
HardTechnical
0 practiced
Describe methods to estimate heterogeneous treatment effects (CATE) across user segments for an A/B test. Cover pre-specified vs post-hoc segmentation, interaction terms in regression, uplift models, and the multiple-testing concerns when searching many segments. For each method, mention how a BI analyst would validate and present the findings.
HardTechnical
0 practiced
Your organization runs hundreds of experiments across dozens of metrics monthly. Explain approaches to control false discoveries at scale: contrast controlling Family-Wise Error Rate (Bonferroni) versus False Discovery Rate (Benjamini-Hochberg). Discuss how you'd group tests into families, implement corrections, and the trade-offs when presenting corrected results to stakeholders.
Unlock Full Question Bank
Get access to hundreds of Experimentation and Product Validation interview questions and detailed answers.
Sign in to ContinueJoin thousands of developers preparing for their dream job.