Start by aggregating the 7 days of data to compute overall conversion rates for each variant, then calculate absolute and relative lift. Next, perform a two-proportion z-test to obtain the p-value and 95% confidence interval, ensuring you check assumptions and interpret results in the context of the experiment.
Pro tip: Always consider the practical significance alongside statistical significance; a small lift with a tiny p-value may not be worth implementing if the confidence interval includes negligible effects.
Sum exposures and purchases for each variant across all 7 days to get total counts. Compute conversion rates as purchases divided by exposures for control and treatment.
Compute absolute lift as the difference in conversion rates (treatment - control) and relative lift as (treatment rate / control rate - 1) * 100%.
Calculate the pooled proportion, standard error, and z-statistic. Use the z-statistic to find the p-value for a two-tailed test.
Calculate the standard error for the difference in proportions and construct the 95% CI using the z-critical value (1.96).
Assess statistical significance (p < 0.05) and practical significance (CI width and effect size). Discuss any limitations or next steps.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining what a sample ratio mismatch (SRM) is and why it matters, then describe how you would check for it both day-by-day and in aggregate using statistical tests like chi-square. If an SRM is found, systematically diagnose potential causes (e.g., instrumentation, randomization, filtering) and propose fixes, emphasizing the importance of resolving SRM before analyzing results.
Pro tip: Always check for SRM before looking at any metrics—if you find one, stop the analysis and treat it as a data quality issue. Document the investigation and communicate transparently with stakeholders, as ignoring SRM can lead to false conclusions.
Explain that SRM occurs when the observed ratio of users in experiment groups deviates from the intended ratio, indicating a flaw in the experiment setup or data collection. It can invalidate results, so it must be addressed before analysis.
Use a chi-square goodness-of-fit test to compare observed vs. expected group sizes each day and overall. Look for consistent deviations or sudden shifts that might indicate a problem.
Investigate common causes: instrumentation issues (e.g., logging errors), randomization bugs (e.g., biased assignment), filtering (e.g., bot removal, outlier exclusion), and data pipeline issues (e.g., delayed data).
Based on the cause, fix the root issue: correct the randomization algorithm, repair logging, adjust filters, or reprocess data. Then re-run the experiment or re-analyze if the fix is applied retroactively.
After fixing, re-check for SRM to ensure resolution. Implement monitoring and automated SRM checks in future experiments to catch issues early.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify that pooling all 7 days is appropriate only if day-level effects are uncorrelated with the treatment and other covariates; otherwise, it can bias estimates. Then, compare fixed-effects (FE) and random-effects (RE) models: FE controls for unobserved day-specific heterogeneity but may lose power, while RE assumes day effects are random and uncorrelated with predictors, allowing for more efficient estimates if assumptions hold. Use a Hausman test to decide between FE and RE, and consider the experimental design and goals.
Pro tip: Mention that in A/B testing, days are often not exchangeable due to day-of-week effects, so ignoring them can inflate false positives; but if you include day fixed effects, ensure you have enough within-day variation to estimate treatment effects precisely.
Determine if the analysis aims to estimate an overall treatment effect or day-specific effects, and note that data are panel-like with repeated observations per day.
Explain that FE treats day effects as parameters to estimate (controlling for all time-invariant day characteristics), while RE treats day effects as random draws from a distribution, assuming they are uncorrelated with treatment.
Discuss whether day effects are likely correlated with treatment (e.g., if treatment assignment varies by day) and the bias-variance trade-off: FE is consistent but less efficient; RE is efficient but biased if assumptions fail.
Recommend the Hausman test to compare FE and RE estimates; if the null hypothesis of no correlation is rejected, FE is preferred.
Based on the context (e.g., Amazon A/B test with random assignment), argue that pooling may be appropriate if days are exchangeable, but often include day fixed effects to control for day-of-week patterns, and check robustness.
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The September 1st spike is screaming seasonality or a holiday effect, so that was the easy one.
Start by clarifying the experiment's design and metrics, then systematically identify pitfalls across statistical, operational, and business dimensions. For each pitfall, propose a concrete guardrail that prevents or mitigates it, emphasizing Amazon's bias for action and customer obsession.
Pro tip: Frame guardrails as pre-launch checks that balance speed and rigor, and mention Amazon's 'two-way door' decisions to show you can move fast without compromising on learnings.
Ask questions to understand the experiment's goal, metrics, duration, and population to tailor your pitfalls and guardrails.
Discuss issues like low power, peeking, multiple comparisons, and novelty effects that can invalidate results.
Cover problems such as sample ratio mismatch, instrumentation errors, and cannibalization that affect execution and business impact.
For each pitfall, suggest specific guardrails like power analysis, sequential testing, A/A tests, and pre-registration.
Highlight the most critical pitfalls and guardrails, and explain how they enable faster, safer experimentation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the available data (sample size, variance, confidence interval, p-value) and compute the achieved power or minimum detectable effect (MDE) to assess sufficiency. Then, weigh statistical significance against practical significance and business impact to recommend rollout, iterate, or extend.
Pro tip: Always connect the statistical decision to business metrics (e.g., revenue impact) and consider the cost of false positives/negatives; this shows you think like a product owner, not just a statistician.
Ask for or state the sample size, variance, confidence level, and observed effect size. Confirm that the +5% relative lift is statistically significant (e.g., p < 0.05) and note the confidence interval.
Calculate the achieved power given the sample size and observed effect, or compare the observed effect to the pre-computed MDE. If power is low (<80%) or the CI is wide, the test may be underpowered.
Determine if the lift is meaningful for the business (e.g., revenue, user engagement). Consider implementation costs, risks, and whether the effect is likely to persist.
If powered and practically significant, roll out. If underpowered but promising, extend the test. If powered but lift is small or negative, iterate on the feature or hypothesis.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.