← Pinterest Interview Insights
I went straight to the two-sample t-test and talked through normality and equal variance assumptions.
Start by clarifying the experiment design and metric distribution, then propose a test like Welch's t-test or Mann-Whitney U depending on normality and variance. Walk through assumptions (independence, normality, equal variance) and how you'd verify them using plots and tests, and mention alternatives if assumptions fail.
Pro tip: At Pinterest, where engagement metrics are often skewed and heavy-tailed, emphasize robust methods like bootstrapping or quantile tests, and discuss how you'd handle multiple testing and practical significance.
Confirm randomization, sample size, and whether active minutes is a continuous or count metric. Check for any clustering or repeated measures.
Verify independence, normality (e.g., Q-Q plots, Shapiro-Wilk), and homogeneity of variance (e.g., Levene's test). Consider transformations or non-parametric tests if violated.
If assumptions hold, use Welch's t-test (unequal variances) or Student's t-test. If not, use Mann-Whitney U or bootstrapping. For skewed data, consider quantile regression or zero-inflated models.
Report effect size, confidence intervals, and p-value. Discuss practical significance in the context of Pinterest's business metrics.
Address multiple comparisons, sequential testing, and potential novelty effects. Mention sensitivity analyses.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the experiment design and metrics, then walk through the calculation of the p-value and confidence interval using the appropriate statistical test. Finally, explain the practical interpretation of each in the context of Pinterest's product decisions, emphasizing that statistical significance does not imply practical significance.
Pro tip: Always relate the results back to business impact—Pinterest cares about whether the change drives meaningful user engagement, not just whether it's statistically significant. Mention that you'd check for novelty effects and segment-level differences before making a ship decision.
Ask about the metric (e.g., click-through rate, saves), sample size, and whether it's a one-tailed or two-tailed test. Confirm the null and alternative hypotheses.
Use the appropriate test (e.g., two-proportion z-test for rates) to compute the p-value. Explain that it's the probability of observing the data (or more extreme) assuming the null hypothesis is true.
Compute the confidence interval for the difference in means or proportions. Explain that it provides a range of plausible values for the true effect size with 95% confidence.
Discuss what the p-value tells you about statistical significance and what the CI tells you about the magnitude and precision of the effect. Highlight that a p-value < 0.05 does not guarantee practical importance.
Explain how you would use these results to make a recommendation, considering factors like effect size, business impact, and potential risks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Multiple comparisons was the part I was least prepared for.
Start by defining Type I and Type II errors in the context of the experiment, emphasizing the trade-off between false positives and false negatives and how they relate to business impact. Then, address the multiple comparisons problem by discussing correction methods like Bonferroni or Benjamini-Hochberg, and how to prioritize metrics based on pre-registration and business relevance.
Pro tip: At Pinterest, where engagement metrics are key, emphasize that you would pre-register the primary metric and use a hierarchical testing approach for secondary metrics to balance statistical rigor with actionable insights.
Explain Type I error as a false positive (concluding a difference exists when it doesn't) and Type II as a false negative (missing a real effect). Relate them to the experiment's context, such as testing a new feature's impact on user engagement.
Highlight how the choice of significance level (alpha) and power (1-beta) affects these errors. For example, a lower alpha reduces Type I errors but increases Type II errors, potentially missing valuable features.
Acknowledge that testing five secondary metrics inflates the family-wise error rate. Mention correction methods like Bonferroni (conservative) or Benjamini-Hochberg (controls false discovery rate) to maintain statistical validity.
Emphasize the importance of pre-registering the primary metric and clearly defining secondary metrics as exploratory. Suggest using hierarchical testing or grouping metrics to reduce the number of comparisons.
Propose a balanced approach: use a stricter alpha for the primary metric, apply corrections for secondary metrics, and consider Bayesian methods or sequential testing to allow for continuous monitoring without inflating error rates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.