I went with GMV per user as primary and latency plus user retention as guardrails.
Start by clarifying the experiment's goal and scope, then define the randomization unit and primary metric (GMV) with clear success criteria. Next, outline guardrail metrics to monitor for unintended negative effects, and explain how you would analyze results with statistical rigor, including power analysis and potential pitfalls.
Pro tip: Emphasize the importance of pre-registering the analysis plan and guardrail thresholds to avoid p-hacking and ensure trustworthy results. Also, consider Snapchat's unique context: short session times and ephemeral content may require careful metric definition and sensitivity to novelty effects.
Confirm the goal: measure the causal impact of the new recommendation algorithm on GMV. Define the population, duration, and randomization unit (e.g., user-level).
Choose GMV as the primary metric, but specify how it's calculated (e.g., per user, total) and set a minimum detectable effect (MDE) based on business needs. Conduct power analysis to determine sample size and duration.
Identify metrics that ensure the algorithm doesn't harm user experience or other business goals, such as user engagement (DAU, time spent), retention, ad revenue, and content diversity. Set acceptable thresholds for degradation.
Decide on A/B split (e.g., 50/50), ensure proper randomization to avoid bias, and consider stratification if needed. Plan for data collection and instrumentation.
Use appropriate statistical tests (e.g., t-test, bootstrap) to compare GMV between groups, check guardrail metrics, and consider novelty effects. Interpret practical significance and recommend rollout or iteration.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Walked through the two-proportion z-test formula out loud.
Start by clarifying the test design (two-sided vs one-sided, equal allocation) and then walk through the standard sample size formula for comparing two proportions. Convert the relative lift to an absolute difference, plug in the baseline rate, alpha, and power, and compute the required sample size per variant. Finally, discuss practical considerations like rounding up and adjusting for multiple comparisons or sequential testing.
Pro tip: Mention that you would validate the calculation using a power analysis tool or simulation, and emphasize that the sample size is a minimum—actual required size may be larger due to real-world factors like non-compliance or novelty effects.
Confirm whether the test is two-sided or one-sided, the allocation ratio (e.g., 50/50), and the primary metric (conversion rate). State that you assume independent observations and no peeking.
Calculate the absolute lift: 3% baseline * 7% relative lift = 0.21 percentage points, so the treatment conversion rate is 3.21%.
Use the formula: n = (Z_{α/2} + Z_{β})^2 * (p1*(1-p1) + p2*(1-p2)) / (p2 - p1)^2, where p1=0.03, p2=0.0321, Z_{α/2}=1.96, Z_{β}=0.84. Compute n per variant.
Plug in the numbers: variance term ≈ 0.03*0.97 + 0.0321*0.9679 ≈ 0.0601; difference squared = (0.0021)^2 = 4.41e-6; numerator (1.96+0.84)^2 = 7.84; n ≈ 7.84 * 0.0601 / 4.41e-6 ≈ 106,800 per variant. Round up to the nearest integer.
Mention that this is the minimum sample size; consider adjustments for multiple metrics, sequential testing, or expected data loss. Suggest validating with simulation or tools like power.prop.test in R.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Novelty effect I covered by suggesting a holdout period and checking if lift decays over time.
Start by acknowledging that novelty effects and seasonality can bias A/B test results, then outline a structured approach to detect, mitigate, and adjust for these issues. Emphasize proactive design choices and post-hoc validation to ensure robust conclusions.
Pro tip: Use a holdout group or switchback testing to isolate novelty effects, and always validate with pre-experiment data to check for seasonality patterns. This shows you think beyond standard A/B testing and consider long-term validity.
Analyze pre-experiment trends and run AA tests to identify baseline seasonality. Monitor early vs. late experiment periods to spot novelty effects.
Use randomization, stratification, or blocking to balance seasonality. Consider switchback or holdout designs to isolate novelty effects.
Track metrics over time and use statistical methods like CUPED or time-series decomposition to adjust for imbalances during analysis.
Run sensitivity analyses, check for heterogeneous treatment effects, and if needed, extend the experiment or run follow-up tests to confirm findings.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the part I found most interesting.
Start by acknowledging that a flat primary metric with improved secondary metrics is a common and nuanced outcome in A/B testing. Then, systematically evaluate the validity of the experiment, the sensitivity of the primary metric, and the potential for secondary metrics to indicate meaningful user behavior changes. Finally, communicate the results with a balanced view, proposing next steps such as deeper analysis or follow-up experiments.
Pro tip: Always check if the primary metric is a lagging indicator or if the experiment duration was sufficient to detect changes; sometimes secondary improvements signal future primary gains. Also, consider that at Snapchat, engagement metrics like time spent or snaps sent per user can be leading indicators of long-term retention.
Check for any issues with the experiment setup, such as sample size, randomization, or metric definitions, that could explain the lack of primary metric movement.
Determine if the primary metric is sensitive enough to detect the expected effect and if the experiment ran long enough to capture it, considering novelty effects or delayed impact.
Analyze whether the secondary engagement improvements are statistically significant, practically meaningful, and aligned with the product's goals, and if they could eventually influence the primary metric.
Present the findings transparently, highlighting both the lack of primary effect and the positive secondary signals, and discuss potential reasons and implications for the product.
Propose actions such as running a longer experiment, refining the primary metric, or conducting further analysis to understand the disconnect between primary and secondary metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.