This felt manageable at first and then kept expanding.
Start by clarifying the business objective and defining a clear, measurable hypothesis about how PayPal cashback influences Walmart customer behavior. Then outline a rigorous experimental design including randomization, control, metrics, and analysis plan, while addressing practical constraints like network effects and seasonality.
Pro tip: Emphasize the importance of pre-registering the analysis plan and defining guardrail metrics to avoid p-hacking and ensure results are actionable. Also, consider the two-sided marketplace dynamics between PayPal and Walmart.
Articulate the specific value proposition: e.g., PayPal cashback increases Walmart purchase frequency or basket size. Formulate a testable hypothesis with clear success metrics.
Choose randomization unit (e.g., user), determine sample size and power, select control and treatment groups, and decide on cashback structure (e.g., percentage vs. fixed amount).
Define primary metric (e.g., incremental revenue or profit for Walmart/PayPal) and secondary metrics (e.g., conversion rate, AOV). Include guardrail metrics like customer satisfaction and PayPal transaction fees.
Run the test for a sufficient duration to capture full business cycles, monitor for data quality and novelty effects, and ensure no contamination between groups.
Perform statistical analysis (e.g., t-test, regression) to measure effect size and significance. Evaluate ROI and make a recommendation based on both statistical and practical significance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with average order value as primary, repeat purchase rate as secondary, and PayPal transaction fee margin as a guardrail so you're not buying Walmart's growth at PayPal's expense.
Start by clarifying the experiment's goal and the specific product change being tested, then define metrics in a hierarchical structure: primary metric directly tied to the hypothesis, secondary metrics for broader impact, and guardrail metrics to ensure no harm. Explain the rationale for each choice, linking to business objectives and statistical considerations like power and sensitivity.
Pro tip: Always tie guardrail metrics to potential unintended consequences specific to PayPal, such as fraud rates or customer trust, and mention how you'd monitor them for early stopping if degradation is severe.
Restate the experiment's goal and hypothesis to ensure alignment. Identify the key user behavior or business outcome the change aims to influence.
Choose one metric that directly measures success of the hypothesis. It should be sensitive to the change and tied to the core objective (e.g., conversion rate, revenue per user).
Pick 2-3 metrics that capture broader impact or potential trade-offs (e.g., engagement, retention, average order value). These help understand the full picture but are not the main decision drivers.
Identify metrics that should not degrade, such as latency, error rates, fraud, or customer satisfaction. These protect against unintended negative consequences.
Explain why each metric was chosen, how they relate to business goals, and how you'll monitor them (e.g., sequential testing, guardrail thresholds).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Standard power analysis stuff but I got tripped up explaining p-value interpretation in plain language.
Start by outlining a structured power analysis process: define hypotheses, choose effect size, set significance level and power, calculate sample size, and consider practical constraints. Then explain how to interpret the p-value in context, emphasizing that it measures evidence against the null hypothesis, not the probability that the null is true or the effect size.
Pro tip: Mention that at PayPal, where experiments run at massive scale, even tiny effects can be statistically significant, so always pair p-values with effect sizes and confidence intervals to assess practical significance.
Clearly state the null and alternative hypotheses, and identify the primary metric (e.g., conversion rate) and any guardrail metrics. This sets the foundation for the power analysis.
Estimate the minimum detectable effect (MDE) based on business relevance, and gather baseline variance or conversion rate from historical data. This drives the sample size calculation.
Choose alpha (typically 0.05) and power (typically 0.80), balancing Type I and Type II error risks. Consider multiple testing corrections if needed.
Use power analysis formulas or tools to compute required sample size per variant, then translate to experiment duration based on traffic. Adjust for practical constraints like seasonality.
After running the test, interpret the p-value as the probability of observing data as extreme or more extreme, assuming the null is true. Compare to alpha to decide statistical significance, but also consider effect size, confidence intervals, and business impact.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about checking for segment heterogeneity, looking at whether the effect exists in a subgroup, and revisiting the MDE assumption itself.
Start by clarifying that results below the MDE don't necessarily mean the experiment failed—they may indicate the effect is smaller than expected or the test lacked power. Then walk through a structured diagnostic: check for validity issues, assess statistical power, and decide whether to iterate, extend, or pivot based on business impact and cost.
Pro tip: Emphasize that you would never simply accept a null result without first checking for common pitfalls like sample ratio mismatch, novelty effects, or instrumentation bugs—this shows rigor and prevents false negatives.
Check for data quality issues, sample ratio mismatch, and whether the experiment ran as designed. Rule out bugs or external factors that could have diluted the effect.
Re-evaluate whether the MDE was realistic given the observed variance and sample size. Determine if the test was underpowered to detect a smaller, yet meaningful, effect.
Look beyond the primary metric: check secondary metrics, segment-level results, and guardrail metrics. A significant effect in a key segment might justify further investigation.
Based on the diagnosis, choose an action: extend the test, increase sample size, refine the hypothesis, or conclude no effect. Consider business impact and cost of further testing.
Share findings with stakeholders, document learnings, and update priors. Even a null result provides valuable information for future experiments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where they wanted me to talk about covariate adjustment approaches for variance reduction.
Acknowledge the underpowered constraint and propose alternative methods to extract valid insights without extending the timeline. Focus on techniques like sequential testing, variance reduction, and Bayesian methods, while emphasizing the trade-offs and the importance of pre-registration to avoid p-hacking.
Pro tip: Mention that you would pre-register the analysis plan and use a holdout group to validate findings, showing rigor and awareness of ethical experimentation.
Restate the underpowered situation and confirm the primary metric and minimum detectable effect (MDE) to understand what 'valid result' means in this context.
Use methods like CUPED (Controlled-experiment Using Pre-Experiment Data) or stratification to reduce variance and increase effective power without more data.
Propose sequential testing, Bayesian methods, or bootstrapping to make valid inferences from limited data, while adjusting for multiple comparisons.
Incorporate historical data or prior experiments to inform priors or validate findings, and consider combining results with other similar experiments.
Clearly state the limitations of the analysis, recommend follow-up experiments if possible, and suggest decision-making based on the confidence intervals and effect sizes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.