Start by defining the goal of reducing pre-roll ad frequency and the key metric (e.g., viewer retention or ad load). Then, discuss the trade-offs between randomizing at the viewer level versus the stream/session level, considering interference and network effects. Finally, propose a robust approach like viewer-level randomization with exposure stickiness and methods to handle cross-stream and creator audience overlap.
Pro tip: Consider using a cluster-randomized design at the creator level if spillover effects are strong, but be mindful of reduced power; alternatively, use viewer-level randomization with exposure time as a covariate to adjust for carryover effects.
Clarify the primary metric (e.g., viewer retention, ad recall) and guardrail metrics (e.g., streamer revenue, viewer satisfaction). This guides the choice of randomization unit.
Compare viewer-level, session-level, and creator-level randomization. Viewer-level minimizes confounding but may violate independence due to shared creators; creator-level reduces interference but may require more clusters.
For viewers switching streams, use viewer-level randomization with exposure stickiness (e.g., assign a viewer to a variant for the entire test period). For creator audience overlap, consider creator-level randomization or model the network effects.
Implement a consistent assignment mechanism (e.g., hashing viewer ID) that persists across sessions and days. Monitor for variant switching and exclude inconsistent exposures.
Use cluster-robust standard errors or mixed-effects models to account for clustering. Consider CUPED or exposure-based analysis to increase sensitivity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Watch time per viewer per day felt obvious as the primary metric.
Start by defining the experiment's goal and the primary metric that directly measures success, then outline guardrail metrics to monitor for unintended harm. For heavy-tailed watch time data, discuss transformations, robust statistical methods, or alternative metrics, and explain the implications for inference such as using bootstrapping or non-parametric tests.
Pro tip: Emphasize that the choice of primary metric should align with the product's north star and be sensitive to the change; for heavy tails, consider using a trimmed mean or percentile-based metric to reduce variance and improve power.
Clarify the hypothesis and select a primary metric that directly measures the intended effect, such as average watch time per user or total watch time.
Choose metrics that ensure the change doesn't harm other key aspects, such as user retention, churn rate, ad click-through rate, or streamer revenue.
Discuss methods to handle heavy tails, such as log transformation, winsorizing, using median or trimmed mean, or applying bootstrapping for inference.
Describe how heavy tails affect statistical power and type I error, and justify the chosen method (e.g., non-parametric tests, robust standard errors) for valid inference.
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First, compute the required sample size per variant using the standard formula for comparing two means with a two-sided test, incorporating the design effect. Then, convert the sample size from viewers to viewer-days by dividing by the average number of days each viewer is exposed during the experiment, ensuring the total available viewer-days (5M viewers × experiment duration) is sufficient.
Pro tip: Always clarify the unit of randomization and analysis: if viewers are randomized, the sample size is in viewers, but if randomization is at viewer-day level, the design effect and clustering must be handled differently. Also, consider that the baseline mean and standard deviation are in minutes, so convert the target lift to absolute minutes before calculation.
Extract baseline mean (36 min), standard deviation (60 min), design effect (1.3), alpha (0.05 two-sided), power (80%), and relative lift (2%). Convert relative lift to absolute difference: 0.02 * 36 = 0.72 minutes.
Use the formula for comparing two means: n = (Z_{α/2} + Z_β)^2 * (σ^2 + σ^2) / Δ^2, where Z_{α/2}=1.96, Z_β=0.84, σ=60, Δ=0.72. This gives n ≈ 2 * (1.96+0.84)^2 * 3600 / 0.5184 ≈ 2 * 7.84 * 3600 / 0.5184 ≈ 108,888 viewers per variant.
Multiply the sample size by the design effect (1.3) to account for clustering or repeated measures: 108,888 * 1.3 ≈ 141,554 viewers per variant.
Determine the average number of days each viewer contributes during the experiment. If the experiment runs for D days and viewers are exposed daily, then viewer-days per variant = n * D. Alternatively, if the sample size is already in viewer-days, ensure the unit matches. Typically, for a daily metric, the sample size in viewer-days is the same as the number of viewers if each viewer contributes one day. Clarify with the interviewer.
Check that the required sample size per variant does not exceed the available 5 million daily mobile viewers. If it does, consider increasing the experiment duration or relaxing the MDE.
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CUPED question, pretty standard if you've seen it before.
Start by explaining the concept of using pre-experiment data as a covariate to reduce variance in A/B tests, then describe the specific regression model (e.g., ANCOVA) and how to apply the coefficient to adjust the outcome metric. Emphasize the practical benefits like increased power and sensitivity, and mention potential pitfalls such as overfitting or violations of assumptions.
Pro tip: Highlight that while variance reduction is powerful, it's crucial to pre-register the use of pre-experiment data and the exact model to avoid p-hacking and ensure valid inference. Also, consider the trade-off between variance reduction and bias if the pre-experiment period is not representative.
Describe how pre-experiment data (e.g., user's historical metric) can explain post-experiment variation, reducing residual variance and increasing power.
Detail the regression: post-experiment metric ~ treatment + pre-experiment metric (and possibly other covariates). Mention that this is an ANCOVA model.
Explain that you fit this regression on the experiment data (or a holdout) to estimate the coefficient (beta) for the pre-experiment metric.
Describe how to adjust each user's post-experiment metric: adjusted_metric = post_metric - beta * (pre_metric - mean_pre_metric). This centers the pre-metric.
Perform the treatment effect analysis (e.g., t-test) on the adjusted metric, which has lower variance, leading to more precise estimates.
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Start by defining the experiment's primary metric and the two-week timeline, then choose a sequential testing method (e.g., alpha spending or group sequential boundaries) that controls type I error across interim looks. Specify the number and timing of interim checks, the stopping rules (e.g., O'Brien-Fleming or Pocock boundaries), and how you'll adjust for multiple comparisons. Finally, discuss practical considerations like traffic volume, minimum detectable effect, and trade-offs between early stopping and statistical power.
Pro tip: Emphasize that you would pre-register the sequential plan and stopping boundaries to avoid p-hacking, and mention that Twitch's high-traffic environment allows for more frequent interim looks without sacrificing power—but you must balance that against operational risks of early stopping.
Specify the primary metric, expected effect size, daily traffic, and the two-week duration. Determine the number of interim looks (e.g., daily or every 3 days) based on traffic and desired power.
Select a method that controls type I error, such as group sequential boundaries (O'Brien-Fleming, Pocock) or alpha spending functions (Lan-DeMets). Justify the choice based on trade-offs between early stopping and power.
Define the exact boundaries for each interim look (e.g., z-scores or p-value thresholds) and the final analysis. Include rules for futility (stopping for no effect) and efficacy (stopping for success).
Explain how the chosen method adjusts the overall alpha level across looks, ensuring the family-wise error rate remains at 5%. Mention any adjustments for multiple metrics or subgroups.
Address operational aspects: how to monitor data quality, handle peeking by stakeholders, and communicate results. Highlight trade-offs like increased sample size or reduced power if using conservative boundaries.
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Filters for bots and AFK viewers felt manageable: session length thresholds, interaction rate signals, IP clustering.
Start by framing the problem around experiment integrity: define what constitutes invalid traffic (bots, AFK viewers) and bias sources (creator-led raids, spillover). Then propose a layered detection and mitigation strategy, combining statistical methods with product/engineering solutions, and emphasize proactive monitoring and sensitivity analyses.
Pro tip: Mention that you would pre-register exclusion criteria and run sensitivity analyses to show robustness, and that you'd collaborate with Trust & Safety and engineering to implement real-time filters without harming legitimate users.
Identify bot and AFK patterns using behavioral signals (e.g., session duration, interaction rates, IP/device anomalies) and statistical thresholds. Use unsupervised methods like clustering or isolation forests to flag outliers.
Apply filters to remove flagged traffic, but validate by comparing results with and without exclusions. Pre-register rules to avoid p-hacking and monitor false positive rates.
Detect raids via sudden spikes in viewership from a single referrer. Treat raids as a separate covariate or stratify randomization by creator to isolate their effect. Consider excluding raid periods or using CUPED with raid as a covariate.
Test for spillover by checking for interference between variants (e.g., via network analysis or comparing cluster-randomized vs. individual-randomized results). If detected, switch to cluster randomization or use switchback designs.
Set up dashboards to monitor traffic quality and spillover metrics in real-time. Document learnings and refine detection algorithms continuously.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.