I went with user-level randomization pretty quickly but fumbled a bit on the contamination angle.
Start by clarifying the goal of the experiment and the constraints of the email campaign, then propose a randomization unit (e.g., user-level) and stratification blocks (e.g., by region or engagement level) to reduce variance. Address contamination by discussing techniques like unique send IDs, deduplication, and coordination with other campaigns to avoid overlap.
Pro tip: Mention that you would use a 'holdout' group to measure long-term effects and that you would monitor for spillover effects via network analysis, especially in a ride-sharing context where user interactions can influence each other.
Understand the experiment's goal (e.g., increase engagement, revenue) and constraints like send frequency, legal requirements, and existing campaigns. This informs the randomization and stratification strategy.
Select the unit of randomization (e.g., user, device, or email address) based on the experiment's scope and potential interference. For email campaigns, user-level randomization is common to avoid within-user contamination.
Identify key covariates (e.g., geography, user activity, past engagement) that correlate with the outcome and create strata to ensure balanced groups. Use stratified randomization to assign users within each block.
Implement deduplication logic to ensure each user receives only one version of the email. Use unique campaign IDs and suppress users who were already sent the email in the same week.
Coordinate with other teams to avoid overlapping campaigns. Use a centralized system to track user exposure and exclude users from multiple experiments if necessary, or use factorial designs if overlap is unavoidable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clearly stating the assumptions: baseline CTR p1=0.03, absolute lift d=0.0018, so p2=0.0318, two-sided alpha=0.05, power=0.80. Then present the standard formula for sample size per variant using the normal approximation, and compute the required sample size. Finally, discuss practical considerations like traffic allocation, duration, and potential pitfalls.
Pro tip: Mention that this calculation assumes independent observations and no clustering, but in practice at Uber you must account for intra-user correlation (e.g., multiple sessions per user) which can inflate required sample size; also consider using sequential testing or CUPED to improve sensitivity.
Clearly define baseline CTR (p1=0.03), absolute lift (d=0.0018), resulting treatment CTR (p2=0.0318), significance level (alpha=0.05, two-sided), and power (1-beta=0.80).
Use the standard formula for comparing two proportions: n per group = (Z_{1-alpha/2} + Z_{1-beta})^2 * (p1(1-p1) + p2(1-p2)) / d^2, where Z values are from the standard normal distribution.
Substitute Z_{0.975}=1.96, Z_{0.80}=0.84, p1=0.03, p2=0.0318, d=0.0018 into the formula and calculate the required sample size per variant.
Round up to the nearest integer, then discuss adjustments for unequal allocation, multiple metrics, or clustering (e.g., by user). Also estimate experiment duration based on daily traffic.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
CTR as primary felt right and I could defend it: opens are too easy to inflate with subject line tricks that don't drive real value, clicks are closer to intent.
Start by clarifying the experiment's goal (e.g., increasing user engagement with email content) and then define a primary success metric that directly measures that goal, with precise numerator, denominator, and time window. Then propose at least two guardrail metrics that ensure the change doesn't harm other important aspects, such as user experience or long-term retention. Finally, justify why CTR is preferred over open rate by highlighting its closer link to actual user interest and downstream actions.
Pro tip: Acknowledge that open rate can be inflated by Apple's Mail Privacy Protection and other tracking limitations, making CTR a more reliable and actionable metric for driving business outcomes.
Restate the experiment's objective to ensure alignment, e.g., 'The goal is to increase user engagement with the email content.'
Specify a metric like Click-Through Rate (CTR): numerator = unique users who clicked at least one link in the email; denominator = unique users who were sent the email; time window = 7 days from send.
Propose at least two guardrails, e.g., Unsubscribe Rate (numerator = unique users who unsubscribed; denominator = unique users who were sent the email; time window = 7 days) and Complaint Rate (numerator = unique users who marked as spam; denominator = unique users who were sent the email; time window = 7 days).
Explain that CTR measures actual engagement and intent, while open rate can be misleading due to privacy features and doesn't guarantee content consumption.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I blanked for a second on the exact name of the alpha-spending approach and just described the concept: you pre-commit a spending function across interim looks so the cumulative false positive rate stays at 5%.
Start by acknowledging the need to balance early stopping with Type I error control, then propose a sequential testing framework like group sequential designs or alpha spending. Emphasize that the plan must be pre-specified, with clear stopping boundaries and adjusted significance levels, and discuss practical implementation considerations.
Pro tip: Mention that you would simulate the operating characteristics (Type I error, power, expected sample size) under various scenarios to validate the design before deployment, and consider using a Bayesian approach with decision rules if stakeholders need continuous monitoring.
Understand what 'harm' means (e.g., negative impact on key metrics) and how frequently leadership wants to peek. Determine the primary metric and acceptable Type I error rate.
Select a method such as group sequential designs with O'Brien-Fleming or Pocock boundaries, or alpha spending functions (e.g., Lan-DeMets) that allow flexible interim looks while controlling overall alpha.
Define the number and timing of interim analyses, stopping rules for harm and futility, and the adjusted alpha at each look. Ensure the plan is documented before the experiment starts.
Run simulations to estimate Type I error, power, and expected sample size under different scenarios. Adjust boundaries if needed to meet requirements.
Set up automated monitoring with alerts when boundaries are crossed. Ensure that any deviation from the plan is justified and documented.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Ran through the usual checks: SRM test on arm sizes, covariate balance across engagement tiers and locale, day-of-week distribution check.
Start by outlining a systematic diagnostic plan that checks sample ratio mismatch (SRM), pre-experiment covariates, and time-based traffic patterns. Then explain how to address issues like instrumentation delays or weekend effects using methods that preserve randomization and avoid peeking biases. Emphasize the importance of pre-registered analysis plans and robust statistical techniques.
Pro tip: Proactively mention that you would set up automated alerts for SRM and other health metrics before the experiment starts, and that any fixes should be applied consistently across all variants to avoid introducing bias.
Run a chi-squared test to compare the observed assignment ratio to the expected ratio. If p-value is very low, investigate potential causes like logging errors or bot traffic.
Compare event counts and timestamps across variants to detect delays or missing data. Check for discrepancies in how metrics are logged between control and treatment.
Segment users by day-of-week, hour-of-day, and other dimensions to identify shifts in traffic composition. Use pre-experiment data to establish expected patterns.
If issues are found, determine if they are random or systematic. For systematic issues, apply fixes uniformly across all variants (e.g., reweighting, filtering) and document changes.
Use methods like CUPED or stratification to adjust for imbalances, and avoid peeking by adhering to pre-registered analysis plans. Consider sequential testing if early stopping is needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Three causes I gave: subject line drives curiosity opens but the email body doesn't deliver on the promise so nobody clicks; bots or preview panes inflating open counts in treatment; or a rendering issue on certain clients causing click tracking to break.
Start by acknowledging the mixed results and framing the need for a careful, multi-faceted analysis. Then, systematically outline plausible explanations, next steps, and how you'd handle ITT vs. PP and stakeholder communication. Emphasize the importance of not overreacting to interim results and considering the broader context.
Pro tip: Mention that interim analyses are often underpowered and that peeking can inflate false positives; suggest pre-registered sequential testing or alpha spending to maintain rigor. This shows statistical maturity and practical experience.
Recognize the mixed results and state that interim looks require caution due to multiple testing and limited sample size. Set the stage for a structured investigation.
List at least three explanations: e.g., novelty effect, metric definition mismatch, segment heterogeneity, instrumentation issues, or external factors. Briefly explain each.
Describe actions like checking data quality, segmenting results, running power analysis, waiting for full data, or conducting qualitative research. Prioritize based on impact.
Explain the difference: ITT includes all randomized users, preserving randomization; PP includes only those who complied. Discuss when each is appropriate and how to handle non-compliance.
Tailor the message to the audience: emphasize caution, avoid premature conclusions, and propose a plan. Use clear, non-technical language and focus on decision-making.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.