I spent too long on the mechanics of the stats and not enough on the story for the PM.
First, acknowledge the mixed results and emphasize the need to understand the trade-off between GB and VC. Then, propose a structured approach to diagnose the underlying reasons and recommend next steps based on business impact and statistical validity.
Pro tip: Frame the recommendation around the company's north star metric and unit economics, showing you understand the business beyond just statistical significance.
Define Gross Booking (GB) and Variable Consideration (VC) and their relationship to key business outcomes. Confirm with the PM whether the goal is to maximize GB, VC, or a combination (e.g., total revenue or profit).
Quantify the trade-off: calculate the net effect on a composite metric (e.g., GB * VC margin) and assess if the GB increase outweighs the VC decrease. Consider segment-level analysis to see if effects vary by user groups.
Explore potential reasons for the VC decrease: e.g., t2 may attract lower-quality borrowers, increase adverse selection, or change pricing dynamics. Check for interactions with other variables and ensure the experiment was properly randomized.
Confirm that the observed changes are statistically significant and not due to chance. Also assess practical significance: is the magnitude of change meaningful for the business?
Based on the analysis, recommend actions: e.g., iterate on t2 to mitigate VC decrease, run further experiments, or abandon t2 if the trade-off is unfavorable. Suggest monitoring long-term effects and considering guardrail metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Net lift is positive so on pure math you'd say launch, but I got tripped up on whether VC is a margin metric or a revenue metric and how to weight the two.
First, clarify what t2 and VC represent (e.g., treatment variants or metrics) and the business context. Then, evaluate the confidence intervals and lifts to assess statistical significance and practical impact, considering trade-offs between GB and VC. Finally, weigh the risks and potential upside to make a recommendation, possibly suggesting further analysis or a phased launch.
Pro tip: Demonstrate that you consider both statistical significance and business impact, and that you're comfortable making decisions under uncertainty. Mention the importance of aligning with business goals and risk tolerance.
Identify what t2 and VC represent (e.g., treatment groups, metrics like Gross Bookings and Variable Contribution) and the business objectives. Understand the units and the significance of the confidence intervals.
For t2, the GB confidence interval is entirely positive, indicating a statistically significant positive effect on GB. For VC, the confidence interval is entirely negative, indicating a statistically significant negative effect on VC. Quantify the expected lifts: +$0.48 for GB and -$0.20 for VC.
Consider the net impact: Does the increase in GB outweigh the decrease in VC? Calculate the net monetary impact if possible, and consider strategic importance of GB vs. VC. Also, consider the confidence intervals: the ranges suggest uncertainty, but both are significant.
Think about potential risks: Is the VC loss acceptable? Could it be mitigated? Are there long-term effects? Consider if a phased launch or further testing could provide more information.
Based on the analysis, decide whether to launch, not launch, or launch with modifications. Justify your decision with data and business reasoning.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Cohort analysis question, fairly standard framing.
Start by clarifying the business context: define t2, GB (likely gross bookings or gross balance), and VC (likely variable cost or variable contribution). Then propose a segmentation strategy that first identifies cohorts where t2 has a positive GB impact, and within those, further segments to isolate where the VC impact is neutral or positive. Use a combination of exploratory analysis, causal inference (e.g., A/B test or observational methods), and statistical testing to validate the segments.
Pro tip: Focus on actionable segments: avoid over-segmenting to the point of noise. Use a holdout or validation set to confirm that the positive GB impact without negative VC impact holds out-of-sample, and consider the cost of targeting each segment.
Confirm what t2, GB, and VC stand for, and the exact business goal (e.g., maximize GB while keeping VC non-negative). Ensure alignment on the time horizon and any constraints.
List potential segmentation variables such as user demographics, order characteristics, t2 exposure levels, and temporal factors. Prioritize dimensions that are likely to interact with t2's effect on GB and VC.
For each segment, estimate the causal effect of t2 on GB and VC using A/B test data or quasi-experimental methods. Look for segments where the GB effect is positive and the VC effect is non-negative.
Use statistical tests and cross-validation to ensure the effects are robust and not due to chance. Refine segments by merging small ones or dropping unstable ones.
Summarize the segments that meet the criteria, along with expected impact and confidence intervals. Suggest next steps for targeting or further experimentation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging the multiple testing problem and the need to balance false discovery control with statistical power. Then outline a structured approach: choose an appropriate error control method (e.g., FDR), consider business impact and power, and implement monitoring and decision rules. Emphasize that launch criteria should be pre-registered and aligned with business goals.
Pro tip: Don't just default to Bonferroni; discuss the trade-off between false positives and false negatives in the context of business risk, and suggest using a false discovery rate (FDR) approach like Benjamini-Hochberg when many tests are run, as it often provides a better balance for product experimentation.
Clarify what decisions will be made based on the experiments (e.g., launch, iterate, kill) and the cost of false positives vs. false negatives. This guides the choice of error control method.
Select a method to control false discoveries, such as Bonferroni, Holm-Bonferroni, or Benjamini-Hochberg (FDR). Consider the number of tests, correlation, and whether you need strong FWER control or can tolerate some false positives.
Ensure each experiment has adequate power to detect meaningful effects after multiplicity adjustment. This may require larger sample sizes or prioritizing fewer, higher-impact tests.
Pre-register the analysis plan, including the error control method and decision thresholds. Monitor experiments for early stopping or futility, using sequential testing or alpha spending if needed.
Interpret results in light of business impact, not just statistical significance. Use confidence intervals and effect sizes to assess practical significance and make reliable launch decisions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.