Start by clarifying the experimental goals and constraints, then evaluate randomization units (user, auction, or geo) based on interference risks. Propose a design that balances validity and practicality, such as cluster randomization with mitigation techniques, and outline how you would measure and adjust for interference.
Pro tip: In marketplace experiments, interference is the norm, not the exception. Consider using switchback or cluster randomization with a holdout to isolate long-term effects, and always pre-register your analysis plan to avoid p-hacking.
Clarify what you're testing (ad load, floor price) and the primary metric (e.g., revenue, user engagement). Identify the level at which the treatment is applied and the level at which outcomes are measured.
Map potential interference channels: auction dynamics (bidders competing across units), supply constraints (ad inventory shared), and user-level spillovers. Quantify the likely direction and magnitude of bias.
Select a unit that minimizes interference while maintaining power. Options include user-level, auction-level, geo-level, or time-based (switchback). Consider cluster randomization if interference is strong.
Implement techniques like holdouts, stratification, or isolation of markets. Run A/A tests and monitor for spillover. Use causal inference methods (e.g., difference-in-differences) if needed.
Account for interference in analysis (e.g., cluster-robust standard errors). Compare results across units and check for heterogeneous effects. Communicate limitations clearly.
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Revenue per user per day as the primary felt obvious.
Start by clarifying the experiment's context and hypothesis, then define a primary KPI that directly measures the intended impact, along with guardrail metrics that ensure no harm to user experience or ecosystem health. Specify exact success and stop-loss thresholds based on statistical power, business impact, and risk tolerance, and explain how you would monitor and act on them.
Pro tip: Tie your thresholds to the minimum detectable effect (MDE) and the cost of a false positive/negative, and mention sequential testing or Bayesian methods to allow early stopping without inflating error rates.
Ask about the specific change being tested, the target population, and the business goal to ensure your metrics align with the intended impact.
Choose a single metric that directly measures the desired outcome (e.g., engagement, revenue) and explain why it is the most sensitive and relevant indicator.
Identify 2-3 metrics that capture potential negative side effects (e.g., user churn, crash rates, latency) and set acceptable bounds to prevent unintended harm.
Determine the minimum lift for success and the maximum acceptable decline for stop-loss, based on statistical power, business impact, and risk tolerance.
Outline how you will monitor metrics over time, including interim analyses, and specify the exact conditions for go, no-go, or iterate decisions.
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They gave you the variance (0.04 USD squared per user per day) so the math is actually doable.
Start by clarifying the metric (daily revenue per user), its distribution, and the test design (e.g., two-sample t-test). Then compute the required sample size per variant using the standard formula for continuous metrics, incorporating variance, minimum detectable effect (10% lift), alpha=0.05, and power=0.90. Finally, translate sample size into traffic split and duration by considering daily traffic and any constraints like novelty effects or seasonality.
Pro tip: Always discuss the assumptions behind the power calculation (e.g., normality, equal variance, no interference) and propose a sanity check with historical data or a pilot. Also, mention that revenue metrics are often skewed, so consider using a transformation or a non-parametric approach if needed.
Confirm that the primary metric is daily revenue per user, a continuous variable. Assume a two-sample t-test with equal variance and two-sided alternative. State the null and alternative hypotheses.
Estimate the baseline mean (μ) and standard deviation (σ) of daily revenue per user from historical data or a pilot. If unavailable, make reasonable assumptions and note them.
Use the formula for continuous metrics: n = 2 * (z_{α/2} + z_β)^2 * σ^2 / Δ^2, where Δ = μ * 0.10 (10% lift). Plug in z_{0.025}=1.96 and z_{0.10}=1.28 (for 90% power).
Decide on a 50/50 split for maximum power. Given daily traffic (number of users eligible), compute duration = (2 * n) / daily_traffic. Adjust for any expected attrition or non-compliance.
Check if the duration is practical (e.g., at least one full week to capture weekly seasonality). If too long, consider increasing MDE, using a more sensitive metric, or running a sequential test. Also, plan for multiple testing corrections if needed.
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Talked through using pre-period revenue as a covariate to reduce variance, which lands well.
Start by framing bias control as a design problem: randomize properly, pre-register metrics, and use pre-experiment covariates to increase sensitivity. Then address seasonality with stratification or time-based controls, and discuss sequential vs fixed-horizon testing trade-offs, including multiple testing corrections for variants. Emphasize that the choice depends on business constraints and the cost of false positives vs false negatives.
Pro tip: At Roblox, where user engagement is highly variable and network effects matter, mention that you'd use CUPED with pre-experiment data to reduce variance, and consider switchback or cluster randomization if interference is a concern. Also, highlight that sequential testing can be gamed if not pre-registered, so you'd set alpha-spending boundaries upfront.
Use pre-experiment covariates (e.g., user activity, tenure) in a regression or CUPED to reduce variance and control for baseline differences. This increases power without introducing bias, as long as covariates are pre-treatment.
Stratify randomization by time or use time-based fixed effects to account for seasonality. Ensure control and treatment groups are exposed to the same temporal patterns, and consider running experiments for full weeks to capture weekly cycles.
Choose fixed-horizon for simplicity and strict error control, or sequential for early stopping. If sequential, use alpha-spending functions (e.g., O'Brien-Fleming) to control Type I error, and pre-register the stopping rule to avoid p-hacking.
For multiple variants, apply corrections like Bonferroni, Holm-Bonferroni, or Benjamini-Hochberg to control family-wise error rate or false discovery rate. Alternatively, use a hierarchical or Bayesian approach to share information across variants.
Run A/A tests to validate the randomization and analysis pipeline, and monitor for sample ratio mismatch (SRM) to detect bias. Document all decisions pre-analysis to ensure reproducibility.
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Start by defining cannibalization and clearing price shifts in the context of the experiment, then outline a diagnostic framework that combines advertiser-level metrics, auction-level data, and control comparisons. Emphasize the importance of segmenting by advertiser type and monitoring both intended and unintended consequences.
Pro tip: Use a difference-in-differences approach with a holdout group to isolate the experiment's causal impact on cannibalization and clearing prices, and always check for novelty effects by analyzing trends over time.
Clearly define what advertiser-side cannibalization and clearing price shifts mean in your experiment context. Formulate hypotheses about how the experiment might cause these effects.
Segment advertisers by size, spend, and historical behavior, then compare key metrics (e.g., ad spend, impressions, CTR, conversion rates) between treatment and control groups.
Examine auction-level data to detect changes in clearing prices, bid density, and win rates. Look for shifts in the distribution of clearing prices and bid competitiveness.
Apply appropriate statistical tests (e.g., t-tests, Mann-Whitney U, or regression with fixed effects) to determine if observed differences are significant and not due to random variation.
Monitor for spillover effects, such as changes in organic engagement or cross-advertiser impacts, and ensure that the experiment doesn't harm the overall ecosystem.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.