The code looks innocent at first glance but there's a lot going on.
Start by scanning the code for common A/B testing pitfalls, such as randomization issues, metric calculation errors, and statistical assumption violations. Then, for each bug, explain its impact on the validity of the experiment and suggest a fix. Prioritize bugs that could lead to false positives or negatives.
Pro tip: Demonstrate statistical rigor by not only identifying bugs but also quantifying their potential impact (e.g., inflated Type I error rate, biased effect size). This shows you understand both code and experimentation.
Verify that users are randomly assigned to control and treatment groups, and that the assignment is independent of user characteristics. Look for issues like non-random assignment, unequal group sizes, or leakage between groups.
Ensure the metric (e.g., conversion rate) is computed correctly, with proper numerator and denominator. Check for issues like including users who didn't trigger the metric, double-counting, or incorrect aggregation.
Confirm that the statistical test used is appropriate for the metric and data distribution. Look for violations of assumptions (e.g., normality, independence) and incorrect application (e.g., using a t-test on binary data without proper adjustment).
Check for missing data, outliers, or filtering that could bias results. Ensure that data cleaning steps are applied consistently across groups and don't introduce bias.
Look for multiple comparisons without correction or early stopping (peeking) that inflates false positive rates. Ensure that the analysis plan accounts for these issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I had a decent answer for this but fumbled the zero-inflated case a bit.
Structure your answer around the nature of the metric (continuous vs. binary), sample size, and distributional assumptions. Explain when each method is appropriate, emphasizing trade-offs between simplicity, robustness, and computational cost. Use concrete examples from A/B testing to illustrate.
Pro tip: Mention that in practice, for large-scale A/B tests, the t-test and proportion test often yield similar results due to the Central Limit Theorem, but the choice matters for small samples or skewed data. Also, highlight that bootstrap is powerful for complex metrics but requires careful implementation to avoid bias.
Determine whether the metric is continuous (e.g., revenue, time spent) or binary (e.g., conversion rate, click-through rate). This guides the choice between t-test and proportion test.
Assess normality, variance homogeneity, and sample size. For large samples, parametric tests are robust; for small samples or non-normal data, consider non-parametric or bootstrap methods.
Weigh simplicity and interpretability (t-test, proportion test) against flexibility and robustness (bootstrap, non-parametric). Bootstrap handles complex metrics but is computationally intensive.
Provide specific scenarios: t-test for continuous metrics with normal-ish data; proportion test for binary metrics; bootstrap for skewed or complex metrics; non-parametric for ordinal or non-normal data with small samples.
Conclude with a concise decision tree or rule of thumb, emphasizing that the choice depends on metric type, sample size, and distributional assumptions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by diagnosing why values are missing—whether it's random or systematic—because that determines the appropriate handling method. Then choose a strategy that preserves statistical validity, such as deletion, imputation, or model-based approaches, and always validate the impact on your test results.
Pro tip: Always check if missingness itself is a metric—if the missing rate differs between control and treatment, that's a signal worth investigating before you impute. Document your missing data handling and run sensitivity analyses to ensure your conclusions are robust.
Determine the mechanism (MCAR, MAR, MNAR) and quantify the missing rate per group. Check if missingness correlates with treatment assignment or other variables.
Select an approach based on the diagnosis: listwise deletion for MCAR with low missingness, imputation (mean/median, regression, multiple imputation) for MAR, or model-based methods for MNAR.
Apply the chosen method, ensuring it's done consistently across groups. Validate by comparing distributions before and after, and check for introduced bias.
Perform the planned tests (e.g., t-test, Mann-Whitney) on the handled data. Consider using methods robust to missing data, like mixed models or bootstrap.
Repeat the analysis with alternative missing data strategies to assess robustness. Report how conclusions change under different assumptions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
SRM check is basically just a chi-squared test on the observed variant split vs the expected split.
Define SRM as a statistically significant deviation between the observed and expected sample sizes in each variant of an A/B test. Explain that it indicates a flaw in the randomization or data collection process, and then outline a systematic approach to detect, diagnose, and handle it.
Pro tip: Emphasize that SRM is a validity threat, not just a metric anomaly—if detected, you should immediately halt the experiment and investigate before trusting any results. Also, mention that prevention is better than cure: implement automated SRM checks in your experimentation platform.
Explain that SRM occurs when the actual ratio of users in each variant significantly differs from the intended ratio (e.g., 50/50). It suggests a bug in assignment, logging, or filtering, and invalidates the test results.
Use a chi-squared goodness-of-fit test or a binomial test to compare observed vs. expected counts. Set a significance threshold (e.g., p < 0.001) and monitor continuously or at the end of the test.
If SRM is detected, check for issues in randomization (e.g., biased hash function), data pipeline (e.g., logging errors), or user segmentation (e.g., bot traffic, uneven exposure).
If SRM is confirmed, stop the experiment, fix the root cause, and rerun the test. If the cause is benign (e.g., known bot filtering), consider adjusting the analysis or excluding affected data.
Implement automated SRM checks in your experimentation platform, use robust randomization methods, and regularly audit data pipelines to catch issues early.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.