I started with the split and stratification, which felt solid, but I fumbled a bit on the guardrails.
Start by framing the experiment around the causal question: does targeting the top 50,000 merchants with a nudge increase adoption compared to no nudge? Then walk through the design choices—randomization unit, stratification, guardrails, and staged rollout—while emphasizing how each choice protects validity and enables actionable inference.
Pro tip: Always pre-register the primary metric and minimum detectable effect, and explicitly state that you will analyze intent-to-treat (ITT) to avoid selection bias from non-compliance. This signals rigor and prevents p-hacking.
Clarify the treatment (sales or email nudge), control (no nudge), primary metric (e.g., subscription adoption rate within 30 days), and secondary metrics (e.g., activation, revenue). Specify the unit of randomization (merchant) and the target population (top 50,000 scored merchants).
Randomly assign merchants to treatment or control within strata defined by score decile and country to ensure balance on these key covariates. Use a 50/50 split or a power-optimized unequal split if treatment is costly. Consider blocking or pairwise matching if sample size is limited.
Define guardrail metrics (e.g., unsubscribe rate, support tickets, merchant dissatisfaction) and non-inferiority thresholds. Pre-specify the minimum detectable effect (MDE) and required sample size. Include a stopping rule for harm or futility.
Start with a small pilot (e.g., 5% of sample) to validate instrumentation and check for technical issues, then ramp to full sample in stages (e.g., 20%, 50%, 100%) while monitoring guardrails. Use sequential testing or alpha-spending to allow early stopping without inflating Type I error.
Analyze intent-to-treat (ITT) using regression adjustment for stratification variables. Report confidence intervals, effect sizes, and heterogeneity by score decile and country. Discuss practical significance and next steps (e.g., rollout, iterate on nudge).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the assumptions (e.g., two-sided test, equal allocation, normal approximation) and then present the standard formula for sample size per arm for comparing two proportions. Plug in the numbers: baseline p1=0.02, p2=0.03 (50% relative uplift), alpha=0.05, power=0.80, and compute n. Show the calculation step-by-step and state the final number clearly.
Pro tip: Mention that this is an approximation and that exact power calculations (e.g., using Fisher's exact test) may yield slightly different results; also note that for low baseline rates, the normal approximation may be less accurate, so tools like G*Power or simulation might be preferred in practice.
Confirm that the test is two-sided, alpha=0.05, power=80%, equal allocation, and that the baseline conversion is 2% with a 50% relative uplift (so absolute uplift = 1 percentage point).
Use the standard formula for sample size per arm for two proportions: n = ( (z_{α/2} + z_β)^2 * (p1(1-p1) + p2(1-p2)) ) / (p2 - p1)^2, where z_{α/2}=1.96 and z_β=0.84.
Compute p1=0.02, p2=0.03, p1(1-p1)=0.0196, p2(1-p2)=0.0291, sum=0.0487. Then n = (1.96+0.84)^2 * 0.0487 / (0.01)^2 = (2.8)^2 * 0.0487 / 0.0001 = 7.84 * 0.0487 / 0.0001 = 0.3818 / 0.0001 = 3818 per arm.
Since sample size must be an integer, round up to 3819 per arm (or 3818 if using exact calculation). State the final number clearly.
Mention that this is an approximation, and that for low baseline rates, exact methods or simulations might be more accurate. Also note that real-world factors like traffic, duration, and multiple testing may affect the required sample size.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward once you set it up as expected value: you need the conversion rate among targeted merchants to exceed 5/120, which is roughly 4.2%.
Start by defining the break-even condition: expected profit per targeted merchant must be non-negative. Then express expected profit in terms of precision, recall, and the costs and margins, and solve for the minimum precision required. Finally, discuss practical considerations like class imbalance and the precision-recall trade-off.
Pro tip: In practice, you also need to consider the opportunity cost of false positives and the potential for repeat purchases beyond 90 days; a simple break-even calculation is just the starting point for a more nuanced business decision.
Set expected profit per targeted merchant to zero: expected revenue minus outreach cost equals zero. This gives the minimum precision needed to avoid losing money.
Let p be the precision (fraction of targeted merchants that convert). Expected profit per targeted merchant = p * $120 - $5. Set this ≥ 0 and solve for p.
Solve p * 120 - 5 ≥ 0 → p ≥ 5/120 ≈ 0.0417. So the model must have precision of at least 4.17% to break even.
Explain that this is a very low threshold, meaning even a weak model can be profitable. Discuss how this relates to the base rate and the importance of recall and coverage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Multi-merchant customers as a contamination vector is genuinely subtle and I think I gave a decent answer about excluding or flagging shared customer IDs.
Start by acknowledging the complexity of multi-merchant contamination and the need for careful experimental design. Then, outline a structured plan to handle pre-existing pilots, multiple outreach touches, and conversion attribution, emphasizing randomization, stratification, and causal inference techniques. Conclude by discussing trade-offs and validation to ensure robust results.
Pro tip: Proactively suggest using merchant-level randomization with a washout period to mitigate contamination, and propose a multi-touch attribution model that accounts for time decay and channel interactions. This shows you think beyond basic A/B testing and understand real-world complexities.
Assess how customers transacting across multiple merchants can lead to treatment spillover. Quantify the potential bias by analyzing customer overlap and merchant networks.
Exclude merchants already piloting Subscription from the experiment or stratify them into a separate cohort. Alternatively, use a difference-in-differences design to account for their prior exposure.
Randomize at the merchant level, stratifying by customer overlap and pilot status. Consider cluster randomization or switchback designs to minimize contamination.
Implement a multi-touch attribution model (e.g., Markov chains, Shapley value) that considers the sequence and timing of outreach. Use holdout groups to measure incremental lift.
Run sensitivity analyses to check for contamination bias. Monitor key metrics and use causal inference methods (e.g., instrumental variables) to validate results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.