Start by framing the problem as a factorial experiment design (2x2) to measure the main effects and interaction of the two simultaneous interventions. Then discuss practical constraints like sample size, overlap, and potential interference, and propose alternative designs if needed. Finally, emphasize the importance of pre-registering metrics and analysis plan to avoid p-hacking.
Pro tip: At LinkedIn, where network effects and interference are common, consider using cluster-based randomization (e.g., by company or region) to minimize spillover. Also, mention that you would check for SUTVA violations and possibly use a switchback or holdout design if overlap is unavoidable.
Identify the two factors: email campaign (yes/no) and in-app messaging feature (yes/no). Determine the unit of randomization (e.g., user, account, or cluster) and ensure it aligns with how each intervention is delivered.
Propose a 2x2 factorial design where users are randomly assigned to one of four groups: control, email only, in-app only, and both. This allows estimation of main effects and interaction.
Check if the design is feasible given sample size, traffic, and potential interference. If not, consider alternative designs like sequential testing, switchback, or using a holdout group for one intervention.
Specify primary and secondary metrics (e.g., engagement, conversion) and pre-register the analysis plan, including how to handle multiple comparisons and test for interaction effects.
During the experiment, monitor for SUTVA violations, sample ratio mismatch, and novelty effects. Use guardrail metrics to ensure no harm.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the metric and hypothesis, then walk through the statistical inputs (baseline, MDE, alpha, power) to compute sample size. Explain how you'd translate that into test duration using traffic and exposure rates, while accounting for practical constraints like novelty effects and seasonality.
Pro tip: Always discuss the trade-off between MDE and sample size—smaller MDEs require exponentially more data, so align with stakeholders on the minimum detectable effect that justifies the test. Also, mention that you'd run a power analysis before the test and monitor for sample ratio mismatch (SRM) during the test.
Identify the primary success metric (e.g., click-through rate, revenue per user) and the null/alternative hypotheses. Clarify if it's a one-tailed or two-tailed test.
Determine baseline conversion rate, minimum detectable effect (MDE), significance level (alpha), and desired power (1-beta). These are needed for sample size calculation.
Use a power analysis formula or tool (e.g., for proportions: n = (Z_alpha/2 + Z_beta)^2 * (p1(1-p1) + p2(1-p2)) / (p1-p2)^2) to compute required sample size per variant.
Divide the total sample size by daily traffic eligible for the test, considering exposure rate and any expected dilution. Adjust for weekly seasonality and avoid stopping early.
Check for sample ratio mismatch (SRM) and ensure assumptions hold. Plan for potential peeking and use sequential testing if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Staggered rollout was my instinct and I think that hurt me a little.
Start by framing the problem: you have two features to test and need to understand both their individual and combined effects. Then compare full factorial and staggered rollouts across dimensions like statistical power, resource needs, and ability to detect interactions. Conclude with a recommendation based on practical constraints and business goals.
Pro tip: Mention that in practice, you often start with a staggered rollout to get a quick read on main effects, then follow up with a factorial design only if interactions are suspected or the features are likely to interact. This shows you balance rigor with speed.
Determine whether the primary goal is to estimate main effects, detect interactions, or optimize the combination. This shapes the design choice.
Explain that it tests all combinations of features (e.g., A/B for each), allowing estimation of main effects and interactions. It requires more traffic and resources but provides comprehensive insights.
Explain that it tests features sequentially or in separate groups, reducing resource needs and complexity but confounding interactions and making it hard to isolate combined effects.
Discuss tradeoffs: full factorial gives interaction estimates but needs more sample size and time; staggered is faster and cheaper but risks missing interactions and can lead to biased conclusions if features interact.
Suggest a decision rule: if interactions are plausible and resources allow, use factorial; if speed is critical and interactions unlikely, use staggered, possibly with follow-up experiments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging that peeking inflates false positive rates, then explain how you mitigate it using methods like sequential testing, alpha spending, or Bayesian approaches. Emphasize that you choose the method based on the experiment's goals and constraints, and always pre-register the analysis plan.
Pro tip: Mention that you also monitor for practical significance and guard against p-hacking by involving stakeholders in pre-defining stopping rules. This shows you balance statistical rigor with business needs.
Explain that sequential testing or peeking increases Type I error rates because of multiple comparisons. Show awareness that this is a common pitfall in A/B testing.
Describe specific techniques like group sequential testing (e.g., O'Brien-Fleming boundaries), alpha spending functions, or always-valid inference (e.g., mixture sequential probability ratio test). Mention Bayesian methods as an alternative that allows continuous monitoring.
Emphasize the importance of pre-defining the analysis plan, including stopping rules, sample size, and primary metrics, to avoid post-hoc rationalization.
Explain how you would implement monitoring with adjusted thresholds, and how you communicate interim results without compromising validity.
Discuss trade-offs between early stopping and statistical power, and how you balance speed with reliability in a business context.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.