This is where I spent the most time and probably said too much.
Start by defining the randomization unit and exposure rule that minimize contamination given overlapping marketing channels, such as randomizing at the user level and defining exposure as ad view. Then address edge cases like users exposed without randomization and cross-platform contamination by using intent-to-treat or instrumental variables, and discuss trade-offs.
Pro tip: Acknowledge that perfect isolation is impossible in multi-channel marketing; instead, focus on quantifying and bounding contamination's impact, and propose sensitivity analyses to validate results.
Select a unit that aligns with the intervention and minimizes spillover, such as user-level for cross-device campaigns, and justify why it's appropriate for the marketing channels involved.
Specify what constitutes exposure (e.g., ad view, click) and how it's measured consistently across channels, ensuring it reflects the intended treatment.
Decide whether to include them in the analysis (e.g., via intent-to-treat) or exclude them, and discuss the bias-variance trade-off and potential use of instrumental variables.
Detect and mitigate users appearing in both variants across platforms by using unique user IDs, cross-device graphs, or probabilistic matching, and consider excluding or adjusting for them.
Run diagnostics like checking for pre-treatment differences, using placebo tests, or simulating contamination to estimate its impact on effect estimates.
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Went with 14-day purchase conversion as primary since it matched the stated business goal and the pre-period baseline gave a clean reference point.
Start by clarifying the experiment's goal and the product surface (e.g., a new onboarding flow or a feature in the Chime app). Then define a primary success metric that directly measures the intended impact, and select guardrail metrics that protect against unintended harm to user experience, business health, and long-term retention. For each metric, specify the exact computation (numerator/denominator), measurement window (e.g., 7-day or 28-day), and exclusion rules (e.g., internal users, bots, or users with pre-existing conditions).
Pro tip: Tie guardrails to Chime's specific business model—e.g., deposit frequency, direct deposit setup, or fee avoidance—and mention that you'd pre-register the metrics and analysis plan to avoid p-hacking and ensure statistical rigor.
Restate the experiment's goal and the expected user behavior change. Identify the primary decision the experiment will inform (e.g., ship, iterate, or kill).
Choose a metric that directly measures the intended impact and is sensitive enough to detect a meaningful effect. Specify its computation, measurement window, and exclusion rules.
Pick at least three guardrails covering different risk areas: user experience (e.g., support contacts), business health (e.g., revenue or engagement), and long-term retention. For each, define computation, window, and exclusions.
For each metric, state the time frame (e.g., 7-day, 28-day) and any exclusion criteria (e.g., internal employees, bots, users with incomplete data). Explain why these choices are appropriate.
Recap how the primary and guardrail metrics together will guide the ship/no-ship decision, and mention any statistical considerations (e.g., power, multiple comparisons).
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SRM check via chi-square on assignment counts, threshold at p < 0.01.
Structure your answer by addressing each diagnostic separately: sample ratio mismatch (SRM), instrumentation gaps, novelty effects, and day-of-week seasonality. For each, describe the specific test or threshold you would use and the remediation steps if it fails. Emphasize a systematic, data-driven approach that prioritizes actionable insights.
Pro tip: Always pre-register your diagnostics and thresholds before the experiment starts to avoid p-hacking and ensure objectivity. When an issue is detected, focus on root cause analysis rather than just fixing symptoms.
Run a chi-squared goodness-of-fit test to compare observed vs. expected traffic split. Use a threshold of p < 0.001 or a relative difference > 2%. If SRM is detected, investigate logging, randomization, or filtering issues and consider invalidating the experiment.
Check for missing data, unexpected nulls, or discrepancies between event counts and expected volumes. Use data quality dashboards, anomaly detection, or SQL audits. If gaps exist, trace the data pipeline, fix instrumentation, and re-run the experiment if necessary.
Analyze treatment effect over time (e.g., daily or weekly) to see if it diminishes. Use a t-test or regression with time interaction, or compare early vs. later periods. If novelty is present, extend the experiment or focus on long-term metrics.
Compare metrics across days of the week using ANOVA or visual inspection. Check if treatment effects vary by day. If seasonality is an issue, ensure balanced randomization across days or use stratified analysis.
For each failure, document the issue, assess impact on validity, and decide whether to fix, re-run, or abandon the experiment. Communicate findings to stakeholders and update best practices.
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I led with CUPED using pre-period engagement as the control variate, since we had 14-day baselines and it's computationally straightforward.
Start by clarifying the nature of the concurrent marketing push and its potential impact on the experiment. Then, propose a concrete adjustment method such as using a difference-in-differences approach or including marketing exposure as a covariate, and discuss the trade-offs compared to alternatives like re-randomization or excluding affected users.
Pro tip: Acknowledge that perfect isolation is rare in real-world experiments; demonstrating how you'd quantify and mitigate bias while maintaining statistical power shows maturity. Also, mention the importance of pre-registering the adjustment plan to avoid p-hacking concerns.
Ask clarifying questions about the marketing push: timing, target audience, and whether it was randomized. Assess how it might differentially affect treatment and control groups.
Select a concrete method like difference-in-differences (if pre-period data exists), propensity score weighting, or including marketing exposure as a covariate in your model.
Apply the method, check for balance after adjustment, and run sensitivity analyses to ensure results are robust to different assumptions.
Compare your chosen method to alternatives (e.g., excluding affected users, re-randomizing, or ignoring the issue) in terms of bias, variance, and practicality.
Summarize why your approach balances validity and business constraints, and suggest how to prevent similar issues in future experiments.
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I worked through the binomial variance formula live and got to an absolute MDE somewhere around 0.3 to 0.4 percentage points, which translates to roughly a 5 to 7 percent relative lift at 80% power.
First, compute the minimum detectable effect (MDE) for a two-proportion z-test using the given parameters: baseline conversion rate (p=0.06), alpha=0.05 (two-sided), power=0.80, and equal allocation. Then, compare the required sample size per variant to the available sample size (2.8M total, so 1.4M per variant) to determine if the experiment is adequately powered. Finally, interpret the MDE in relative terms and discuss practical implications.
Pro tip: Always clarify whether the MDE is absolute or relative, and state the assumptions (e.g., normality, independence). Also, mention that with such a large sample, even tiny lifts become detectable, so consider practical significance, not just statistical significance.
List the given values: baseline conversion rate p=0.06, alpha=0.05 (two-sided), power=0.80, allocation ratio=1:1, total sample size N=2.8M. Use the standard formula for sample size per variant for a two-proportion z-test: n = ( (z_{1-alpha/2} + z_{1-beta})^2 * (p1(1-p1) + p2(1-p2)) ) / (p2 - p1)^2, where p2 = p1 + delta.
Since total sample is fixed, solve for the minimum detectable absolute difference delta (p2 - p1) that yields power 0.80 with n=1.4M per variant. Use the formula: delta = (z_{1-alpha/2} + z_{1-beta}) * sqrt( (p1(1-p1) + p2(1-p2)) / n ). Because p2 depends on delta, iterate or approximate by using p1(1-p1) for both groups (conservative).
Convert the absolute MDE to relative lift: relative MDE = delta / p1. For example, if delta ≈ 0.0005, then relative lift ≈ 0.0005/0.06 ≈ 0.83%.
Compare the computed MDE to the effect size you care about. If the MDE is smaller than the smallest meaningful effect, the experiment is adequately powered. With 2.8M users, the MDE will be very small (likely <1% relative), so the experiment is highly powered for typical lifts.
Mention that statistical significance does not imply practical significance. Also, note assumptions: independent observations, no interference, and that the baseline rate is accurate. Consider multiple testing if multiple metrics are evaluated.
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The CI crossing zero was the sticking point for me.
Start by acknowledging the mixed results and the uncertainty in the primary metric, then propose an extended test to gather more data, because the confidence interval includes zero and the refund rate increase is concerning. Explain the decision thresholds you would use, such as statistical significance, practical significance, and guardrail metrics, and how you would communicate the recommendation to stakeholders.
Pro tip: Emphasize that the refund rate increase is a red flag that could erode long-term value, so even if conversion lift becomes significant, you'd need to investigate the root cause before launching. Also, mention that you'd align with product and finance on the acceptable trade-off between conversion and refunds.
Note that the 95% CI for conversion lift includes zero, so the result is not statistically significant. The ARPU increase is positive but small, and the refund rate increase is a potential guardrail violation.
Consider whether a +3.5% lift in conversion is meaningful for Chime, and weigh it against the 1.1pp refund rate increase. Calculate the net impact on revenue and customer lifetime value.
Refund rate is a key guardrail; an increase suggests potential customer dissatisfaction or product issues. Also consider other guardrails like churn, support tickets, or regulatory concerns.
Given the uncertainty and guardrail concern, recommend an extended test to gather more data, possibly with a larger sample size or longer duration, and investigate the refund rate increase.
Present the trade-offs clearly, using non-technical language, and propose next steps: extend test, dive deeper into refunds, and set a decision deadline. Align with product, finance, and leadership on risk tolerance.
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