This is where I started strong and then trailed off.
Start by framing data quality validation as a prerequisite to any analysis, then systematically check each layer: assignment (SRM, bucketing), exposure (logs vs analytics), and metrics (outliers, consistency). Emphasize that you would automate these checks and set thresholds for alerts, and that you would investigate any anomalies before trusting results.
Pro tip: Mention that you would pre-register the expected sample ratio and use a sequential testing framework to avoid peeking, and that you would check for SRM not just overall but also by key dimensions like device or geography to catch subtle issues.
Check if the observed ratio of sessions (or users) between variants matches the expected ratio using a chi-square test. Investigate any deviation (SRM) by segmenting by date, device, or other dimensions to identify the source.
Ensure that the randomization unit (e.g., user ID) is consistently assigned to the same variant across sessions. Check for cross-contamination, such as users appearing in both variants, and validate that the bucketing algorithm is deterministic and properly seeded.
Reconcile the number of users/sessions exposed to each variant from the experiment platform's exposure logs with the counts in the analytics data. Discrepancies may indicate logging issues, filtering, or bot traffic.
Validate that metrics like signups, activation, latency, support tickets, refund rate, and revenue are computed correctly and consistently across variants. Look for outliers, missing data, or impossible values (e.g., negative revenue) that could skew results.
Create a standardized data quality report and automate these checks to run before every experiment analysis. Set thresholds for alerts and document any manual investigations for reproducibility.
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I said day-7 activation as the primary and used revenue-d30 as a secondary.
Start by clarifying the experiment's goal and the decision it will inform, then map available columns to candidate metrics. Choose a primary metric that directly measures the intended impact and is sensitive enough to detect a meaningful effect, and select guardrail metrics that capture potential negative side effects or trade-offs. Justify each choice by linking it to the business objective, statistical power, and the specific columns available.
Pro tip: Always tie your metric choices back to the decision the experiment will drive—interviewers want to see that you think about actionability, not just statistical significance. Also, mention that guardrails should be leading indicators of long-term harm, not just any secondary metric.
Ask or infer what change is being tested and what decision the results will inform (e.g., ship, iterate, or kill). This anchors metric selection to business impact.
Review the provided columns and identify which can be aggregated into metrics that reflect user behavior, revenue, or other outcomes. Note any limitations (e.g., missing data, proxy metrics).
Choose one metric that best captures the intended effect, is sensitive to the change, and aligns with the decision. Justify why it's the most direct and reliable measure given the columns.
Identify 1-3 metrics that could be negatively impacted by the change (e.g., user retention, revenue, latency). Ensure they are measurable from the available columns and represent potential trade-offs.
Explain how you'd ensure adequate power for the primary metric, and how guardrails help detect unintended consequences. Discuss any assumptions or limitations.
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Start by explaining the standard approach for simple proportion metrics using two-proportion z-tests and normal approximation confidence intervals. Then describe how ratio metrics require delta method or bootstrap due to the ratio of random variables. Finally, address skewed revenue by recommending bootstrap, log transformation, or non-parametric methods, and emphasize the importance of using robust techniques like quantile regression or winsorization.
Pro tip: Mention that at companies like Upstart, where revenue is often heavy-tailed, you'd validate results with multiple methods (e.g., bootstrap and delta method) and check for practical significance, not just statistical significance, to avoid overreacting to outliers.
For metrics like conversion rate, use a two-proportion z-test to compare treatment and control. Compute the confidence interval using the normal approximation: (p1 - p2) ± z * sqrt(p1(1-p1)/n1 + p2(1-p2)/n2).
For metrics like revenue per user (total revenue / number of users), the metric is a ratio of two random variables. Use the delta method to approximate the variance, or use bootstrap resampling to empirically estimate the distribution and confidence intervals.
When revenue is heavily skewed, the normal approximation may fail. Use bootstrap methods (e.g., percentile or BCa) to construct confidence intervals, as they don't assume normality. Alternatively, consider log transformation or non-parametric tests like Mann-Whitney U, but be cautious about interpreting transformed effects.
Validate results by comparing multiple methods (e.g., delta method vs. bootstrap) and checking for outliers. Consider winsorizing or trimming extreme values, but report both original and robust results to assess sensitivity.
Beyond statistical significance, assess practical significance by looking at effect sizes, confidence interval widths, and business impact. For skewed metrics, consider median or quantile treatment effects as complementary measures.
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Talked through power calculations and how MDE depends on baseline rate, variance, and sample size.
Start by explaining that the assessment of whether an experiment ran long enough hinges on pre-registered criteria: the stopping rule and the minimum detectable effect (MDE). Then describe how you would monitor the experiment against these criteria, using sequential testing or fixed-horizon methods, and how you would handle deviations or peeking.
Pro tip: Emphasize that the stopping rule must be pre-registered to avoid p-hacking and that you should never stop an experiment early just because results look significant; instead, rely on the pre-defined rule and consider the practical significance via MDE.
Explain that before launching, you specify a stopping rule (e.g., fixed sample size, sequential testing boundaries) and the MDE, which is the smallest effect size you want to detect with sufficient power.
Using the MDE, power (e.g., 80%), significance level (e.g., 5%), and baseline metric variance, compute the required sample size per variant, then translate to duration based on traffic.
Track sample accumulation, check for sample ratio mismatch (SRM), and ensure data quality. Do not peek at outcome metrics unless using sequential methods that adjust for multiple looks.
Once the pre-registered sample size or time is reached, analyze results. If using sequential testing, stop when boundaries are crossed; otherwise, wait until the fixed horizon.
Assess whether the observed effect is statistically significant and practically meaningful relative to the MDE. Consider confidence intervals and business impact.
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Time-sliced analysis: split the experiment period into early and late cohorts and see if the effect attenuates over time.
Start by defining what novelty and learning effects mean in the context of the experiment, then outline specific detection methods such as analyzing time-based trends and segmenting by user experience. Finally, describe analyses like difference-in-differences or cohort analysis to isolate these effects from the true treatment effect.
Pro tip: Emphasize the importance of pre-registering your analysis plan to avoid p-hacking, and consider using holdout groups to measure long-term effects. Also, mention that novelty effects often diminish over time, so plotting treatment effects over time is a simple yet powerful diagnostic.
Clarify that novelty effects occur when users initially engage more with a new feature due to its newness, while learning effects happen as users adapt and their behavior changes over time. Explain how these can confound treatment effects.
Analyze treatment effects over time by plotting metrics across days or weeks. Look for patterns like an initial spike that decays (novelty) or a gradual increase (learning). Use statistical tests to check for significant time-treatment interactions.
Split users into new vs. existing, or by exposure frequency, to see if effects differ. Novelty effects are often stronger for new users, while learning effects may be more pronounced for existing users.
Apply difference-in-differences, cohort analysis, or regression with time interactions to separate these effects. Consider using a holdout group that never receives the treatment to measure long-term trends.
If novelty or learning effects are detected, adjust the analysis by focusing on steady-state periods or using models that account for these dynamics. Communicate the implications for decision-making.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the hardest part because there's no clean formula.
Start by clarifying the primary business objective and the guardrail metrics, then quantify the trade-offs using statistical and practical significance. Propose a decision framework that weighs activation gains against latency and support costs, and recommend a path forward with potential mitigations.
Pro tip: Always tie the decision back to the company's north star metric and consider the long-term impact of latency and support tickets on customer lifetime value. A mature answer acknowledges that sometimes the best decision is to iterate rather than ship or kill outright.
Identify the primary success metric (e.g., activation) and guardrail metrics (latency, support tickets) that must not degrade. Confirm the acceptable thresholds for each based on business goals.
Estimate the expected impact of variant B on the primary metric and guardrails, including confidence intervals. Translate latency and support tickets into monetary terms (e.g., cost per ticket, revenue impact of latency).
Determine if the observed differences are statistically significant and whether they are practically meaningful. Consider the sample size, duration, and potential novelty effects.
Explore ways to reduce latency or support burden without losing activation gains (e.g., engineering optimizations, better onboarding). Decide if a follow-up experiment is needed.
Based on the analysis, recommend ship, no-ship, or iterate. Clearly state the rationale, risks, and next steps, and align with stakeholders.
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I listed traffic source breakdowns, device type splits, and step-level drop-off in the signup funnel.
Start by acknowledging the primary ship decision, then pivot to a structured plan for extracting deeper insights: segment-level heterogeneity, funnel step analysis, and form field sensitivity. Emphasize how these insights can inform product improvements and future experiments, and discuss potential trade-offs in analysis.
Pro tip: Always connect insights back to business impact and next steps—interviewers want to see that you can prioritize actionable findings over interesting but irrelevant ones. Also, mention the importance of checking for novelty effects and ensuring statistical power in segment analyses.
Analyze treatment effects across key user segments (e.g., demographics, behavior, device) to identify differential responses. Use interaction tests or subgroup analysis to determine if the effect varies significantly.
Break down the conversion funnel to see where the treatment impacts each step (e.g., application start, form completion, approval). Identify drop-off points and quantify the contribution of each step to the overall effect.
Examine how individual form fields (e.g., required vs. optional, order, wording) affect completion rates and data quality. Use field-level metrics and possibly qualitative feedback to understand friction.
Combine insights to identify the most impactful areas for improvement, and propose follow-up experiments or product changes. Consider business metrics like conversion, cost, and risk.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.