Choose a metric like 'completed trips per active rider' and define it with precise numerator and denominator, time window, and exclusions. Then design a randomized controlled experiment using switchback or cluster randomization to handle interference, with phased rollout, guardrail metrics, and clear interpretation of confidence intervals for practical significance.
Pro tip: Acknowledge that marketplace metrics often suffer from interference and network effects, so standard A/B tests may be biased; propose a switchback or cluster-randomized design and discuss how you'd validate assumptions.
Specify the metric's numerator, denominator, time window, and any exclusions (e.g., completed trips per active rider per day, excluding canceled trips and new users).
Select a design that accounts for interference, such as switchback (time-based randomization) or cluster randomization (by city or driver), and justify why it's appropriate for the marketplace metric.
Define treatment and control conditions, then plan a phased rollout (e.g., pilot in one city, then expand) to monitor for early signals and operational risks.
Identify guardrail metrics (e.g., driver earnings, rider wait time, cancellation rate) and establish thresholds for acceptable degradation to ensure the experiment doesn't harm the ecosystem.
Analyze the confidence intervals for the treatment effect, considering both statistical and practical significance, and decide whether to launch, iterate, or stop based on the interval's width and position relative to the minimum detectable effect.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Follow-up after I committed to switchback.
Acknowledge that standard A/B tests are the default but explain that switchback designs are necessary when there are interference or spillover effects between units. Focus on the specific context (e.g., Uber's marketplace) where such effects are present, and discuss the trade-offs between the two designs.
Pro tip: Mention that switchback designs can also help with novelty effects and reduce the required sample size by using within-unit comparisons over time, but be careful about time-varying confounders.
Briefly describe what a standard A/B test entails: randomizing units (e.g., users, drivers) into control and treatment groups, assuming no interference between units.
Explain that in Uber's marketplace, units interact (e.g., drivers and riders), so treating one unit can affect others, violating the no-interference assumption (SUTVA).
Describe switchback: randomizing treatment over time periods for the entire system (or geographic area), so all units receive both treatments at different times, mitigating interference.
Discuss pros and cons: switchback reduces interference bias but may introduce time trends, carryover effects, and requires careful analysis; standard A/B is simpler but biased under interference.
Summarize that switchback is preferred when interference is strong (e.g., pricing, dispatch algorithms), while standard A/B is fine when interference is negligible (e.g., UI changes).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Acknowledge that the result is suggestive but not conclusive, and emphasize the importance of considering practical significance, statistical power, and business context. Discuss how you would communicate the uncertainty and decide on next steps, such as running a longer experiment or analyzing segments.
Pro tip: Avoid fixating on the p-value threshold; instead, focus on the effect size and confidence interval to assess practical impact. Frame the result as an opportunity to gather more data or refine the experiment rather than a binary win/lose.
Explain that a 90% confidence level means there is a 10% chance of a false positive, so the effect is not statistically significant at the conventional 95% level. Clarify that this does not mean the effect is absent, but that the evidence is weaker.
Look at the effect size and confidence interval to determine if the observed effect, if real, would be meaningful for the business. Consider the cost of a false positive versus the potential gain.
Evaluate whether the experiment was adequately powered to detect the effect at 95% confidence. If not, the result may be due to insufficient sample size, and extending the experiment could provide clarity.
Rule out issues like multiple testing, peeking, or segment-specific effects that could explain the result. Ensure the experiment was designed and analyzed correctly.
Recommend actions such as running the experiment longer, conducting a follow-up study, or making a decision based on risk tolerance and business priorities. Communicate the uncertainty clearly to stakeholders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pre-picked driver-side income as my guardrail.
Start by clarifying the experiment's primary goal and the potential negative side effects it could cause. Then propose a guardrail metric that directly measures a critical downside risk, explaining why it's the most important to monitor and how you would set thresholds for action.
Pro tip: Choose a guardrail metric that is sensitive to the change and tied to long-term user value, not just a vanity metric. Also, mention that you would monitor multiple guardrails but prioritize one as the primary guardrail based on the biggest risk.
Ask or infer what the experiment is trying to improve and what negative side effects it might inadvertently cause. This sets the context for choosing a relevant guardrail.
Brainstorm metrics that capture potential harms, such as user retention, satisfaction, latency, or revenue. Consider both short-term and long-term impacts.
Choose one metric that best represents the biggest risk to the business or user experience. Explain why it's more important than others in this context.
Specify what level of degradation would be unacceptable and how you would monitor it (e.g., statistical significance, practical significance). Mention what action you would take if the guardrail is breached.
Discuss how you would validate the guardrail's sensitivity and consider if additional guardrails are needed as the experiment evolves.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining the tradeoff between statistical power and operational constraints: shorter windows increase sample size but may introduce interference and carryover effects, while longer windows reduce interference but increase variance and reduce sensitivity. Then propose a data-driven approach that balances these factors, such as using pilot data to estimate autocorrelation and interference decay, and selecting the window length that minimizes mean squared error of the treatment effect estimate.
Pro tip: Mention that in practice, you often run a pilot with varying window lengths to empirically measure carryover and interference, and then choose the shortest window that keeps bias below a pre-specified threshold. This shows you value both rigor and pragmatism.
Identify the primary metric, the desired minimum detectable effect, and operational constraints like switching costs and user experience. This sets the stage for balancing statistical and practical considerations.
Determine how long treatment effects persist and how much interference occurs between units. Use domain knowledge or pilot data to estimate the decay rate of these effects.
Model how window length affects bias (from interference/carryover) and variance (from fewer independent units). Aim to minimize mean squared error of the treatment effect estimate.
Run a pilot experiment with multiple window lengths or simulate data to empirically choose the optimal window. Check that the chosen length keeps bias within acceptable limits.
After deployment, monitor for unexpected interference or changes in user behavior, and be prepared to adjust the window length if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.