This felt manageable at first but I tripped on sample size.
Start by framing the experiment around a clear product goal (e.g., increasing group call engagement) and translate it into a measurable hypothesis. Then systematically walk through each design decision—randomization unit, sample size, duration—justifying each with trade-offs and practical constraints. Emphasize how you would validate assumptions and monitor guardrail metrics.
Pro tip: For social features like group video calling, network effects and interference are critical: consider cluster randomization (e.g., by social graph or group) and discuss how you'd detect and mitigate spillover. Also, mention that you'd run a power analysis upfront and plan for heterogeneous treatment effects across user segments.
State a clear, testable hypothesis (e.g., 'Adding group video calling increases 7-day group call engagement by X%') and define primary, secondary, and guardrail metrics (e.g., call duration, retention, reports).
Decide between user-level, group-level, or cluster randomization based on interference risk. For group video calling, group-level randomization is often preferred to avoid spillover within groups.
Conduct power analysis: specify minimum detectable effect (MDE), significance level (α), power (1-β), and variance. Calculate required sample size per variant, accounting for clustering if applicable.
Balance statistical power with novelty effects and seasonality. Use sample size and expected daily traffic to estimate duration; ensure it covers full weeks and avoids major holidays.
Pre-register analysis plan, including A/A tests, sequential testing if needed, and guardrail monitoring. Define how to handle multiple comparisons and heterogeneous treatment effects.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging that network effects create interference between users, which violates the stable unit treatment value assumption (SUTVA) and can bias standard user-level randomization. Then propose a randomization unit that minimizes interference—such as clusters (e.g., communities, geographic regions) or a switchback design—while balancing statistical power and practical constraints. Finally, discuss trade-offs and potential mitigation strategies like cluster-level analysis or variance reduction techniques.
Pro tip: Mention that you would run a power analysis upfront to quantify the variance inflation from cluster randomization, and consider using a 'cluster-level' metric (e.g., average per cluster) to avoid dilution. Also, highlight that Meta often uses 'network cluster' randomization for social products, so showing awareness of internal practices can set you apart.
Map how network effects cause spillovers: e.g., treatment of one user affects outcomes of connected users through social influence, content sharing, or competition for attention.
List possible units (user, cluster, time-based switchback, geo) and assess each for interference reduction, feasibility, and statistical power.
Select the unit that sufficiently reduces interference while maintaining enough clusters or time periods to detect meaningful effects; often clusters or switchback are preferred.
Use cluster-robust standard errors, aggregate metrics to cluster level, or apply variance reduction (CUPED) to recover power; consider partial randomization if clusters are too few.
Run A/A tests or diagnostic checks to ensure no residual interference, and be prepared to adjust the design if network effects persist.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging that clustering is the gold standard for reducing interference, then pivot to alternative designs that either isolate interference or account for it statistically. Structure your answer around specific techniques like switchback, time-based randomization, or geo-based experiments, and explain when each is appropriate. Emphasize the trade-offs and how you would validate results.
Pro tip: Mention that at Meta, switchback tests are often used for marketplace or social network features where interference is high, and that you can combine designs (e.g., switchback with holdout) to further reduce bias. Also, highlight the importance of measuring interference directly to choose the right design.
Clarify why clustering isn't feasible and what type of interference exists (e.g., network effects, shared resources, temporal spillover). This determines which alternative design is most suitable.
Switchback experiments alternate treatment and control over time periods, reducing interference from user-to-user interactions. Discuss how to choose period length and handle carryover effects.
Randomize at geographic or market level to isolate interference, but account for fewer units and potential confounding. Mention synthetic control or difference-in-differences as analysis methods.
For individual-level interference, use within-subject designs where each user experiences both treatment and control at different times, with washout periods to mitigate carryover.
Use methods like CUPED with interference proxies, or model interference directly. Validate by running A/A tests or comparing with cluster-based results when possible.
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Start by distinguishing between continuous and proportion-based metrics, then outline the appropriate statistical tests for each, emphasizing how the choice depends on sample size, distribution, and variance. Highlight the importance of using the correct test to ensure valid inference and discuss practical considerations like power analysis and multiple testing corrections.
Pro tip: Mention that at scale (like Meta), you often rely on large-sample approximations (e.g., t-tests or z-tests) even for non-normal data due to the Central Limit Theorem, but always check for edge cases like low-frequency events where exact tests or variance stabilization might be needed.
Define continuous metrics (e.g., time spent, revenue) and proportion-based metrics (e.g., click-through rate, conversion rate) and explain why they require different statistical treatments.
For continuous metrics, recommend the two-sample t-test (or Welch's t-test if variances differ) for comparing means. If normality is questionable and sample size is small, consider non-parametric tests like Mann-Whitney U.
For proportions, use a two-proportion z-test or chi-square test. For small sample sizes or rare events, consider Fisher's exact test or Bayesian methods.
Discuss assumptions (independence, normality, homogeneity of variance) and how to handle violations. Mention power analysis, sample size calculation, and corrections for multiple comparisons.
If metrics are skewed or have excess zeros, suggest transformations, bootstrapping, or specialized tests like the Kolmogorov-Smirnov test for distribution shifts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by emphasizing that the core message must remain consistent, but the presentation should be tailored to each audience's priorities and technical depth. For the PM, focus on business impact, actionable insights, and clear visualizations; for the DS peer, dive into methodology, statistical significance, and reproducibility. Use a concrete example from your experience to illustrate the differences.
Pro tip: Always connect the results to the audience's goals—PMs care about product decisions and user impact, while DS peers care about rigor and potential improvements. Show that you can translate technical findings into business value without losing scientific integrity.
Determine what each stakeholder cares about: PMs focus on product metrics, user experience, and next steps; DS peers focus on experimental design, validity, and technical details.
For the PM, lead with the 'so what'—the impact on key metrics and recommended actions. For the DS peer, lead with the 'how'—the methodology, assumptions, and statistical outcomes.
Use simple charts (e.g., bar charts of lift) and business metrics (e.g., conversion rate) for the PM. Use detailed plots (e.g., confidence intervals, distribution plots) and technical metrics (e.g., p-value, effect size) for the DS peer.
For the PM, omit technical jargon and focus on high-level takeaways. For the DS peer, include details on data cleaning, model assumptions, and potential biases.
For the PM, prepare to discuss implications and follow-up experiments. For the DS peer, be ready to defend methodological choices and discuss limitations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.