I went with Instagram's suggested posts in the home feed, which felt like a safe pick.
Choose a well-known consumer app feature that you genuinely dislike, then propose a specific change and define a metric (single or composite) to measure user response. Structure your answer by first describing the feature and your proposed change, then explaining how you would measure success using a metric that captures the desired user behavior. Emphasize the rationale behind your metric choice and how it aligns with business goals.
Pro tip: When defining a composite metric, explain how you would weight its components and validate that it's not gameable. Also, mention how you would set up an A/B test to measure the metric's change, showing end-to-end thinking.
Pick a consumer app feature you dislike and clearly explain why it's problematic from a user or business perspective. This sets the stage for your proposed change.
Describe a concrete modification to the feature that addresses the problem. Be clear about what the change entails and its intended impact on user behavior.
Choose a single or composite metric that directly measures user response to the change. Explain what the metric captures and why it's appropriate.
Explain how the metric aligns with business goals, how you would track it (e.g., via A/B test), and any potential pitfalls or guardrail metrics to consider.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the metric and test design, then use a power analysis formula to compute the required sample size per variant based on baseline rate, minimum detectable effect, significance level, and power. Finally, translate sample size into duration using daily traffic and consider practical constraints like novelty effects and business cycles.
Pro tip: Always discuss the trade-off between MDE and duration: smaller MDEs require exponentially more samples, so align with stakeholders on the smallest effect that justifies the test. Also, mention that you'd validate assumptions with a pilot or historical data to avoid underpowered tests.
Identify the primary metric (e.g., conversion rate, click-through rate) and specify the baseline value, significance level (α), power (1-β), and the minimum detectable effect (MDE) you care about.
Use the standard formula for two-proportion z-test: n = (Zα/2 + Zβ)^2 * (p1(1-p1) + p2(1-p2)) / (p2 - p1)^2, where p1 is baseline and p2 = p1 + MDE. For continuous metrics, use the t-test formula with variance.
Multiply per-variant sample by number of variants (usually 2) to get total sample. Divide by daily traffic eligible for the test to estimate days needed, then round up to full weeks to account for weekly seasonality.
Account for novelty effects, ramp-up periods, and potential data loss (e.g., bot filtering). Consider running at least one full business cycle (e.g., 1-2 weeks) even if sample size is reached sooner.
Sanity-check with historical data or a pilot, and clearly communicate the assumptions and trade-offs (e.g., longer duration vs. smaller MDE) to stakeholders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Said English-speaking markets with high DAU and relatively homogeneous usage patterns, basically to reduce variance.
Start by clarifying the test's goal and success metrics, then propose a specific user segment or geography based on data-driven factors like traffic volume, conversion rates, and strategic importance. Justify your choice by explaining how it balances statistical power, business impact, and risk, and mention how you'd validate and potentially expand the test later.
Pro tip: Show that you think about both statistical validity and business practicality: choose a segment with enough volume to detect meaningful effects, but also consider if the results will generalize to other segments. Mention that you'd run a power analysis to confirm sample size before committing.
Ask or state the primary goal of the test (e.g., increase reviews, clicks, bookings) and the key success metric. This ensures your targeting aligns with what you're trying to learn or optimize.
List candidate segments (e.g., new vs. returning users, mobile vs. desktop, specific cities) and geographies (e.g., high-traffic cities, underperforming markets). Consider factors like traffic volume, baseline conversion, and strategic value.
Estimate whether the segment has enough users to detect a meaningful effect size. Consider running a power analysis or using historical data to check variance and expected lift.
Weigh the potential upside (e.g., revenue, engagement) against risks (e.g., user experience, cannibalization). Choose a segment where a positive result would be impactful and a negative result wouldn't be catastrophic.
Recommend starting with a small, representative segment (e.g., one city or user type) to validate the test, then expanding if results are promising. Mention how you'd monitor and decide to scale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a two-sample t-test for the continuous engagement metric, mentioned checking normality assumptions and that with large enough n the CLT covers it anyway.
Start by clarifying the metric type (e.g., conversion rate, revenue per user) and the experimental design (sample size, randomization). Then recommend a test based on assumptions: for continuous metrics use Welch's t-test if normality holds, otherwise Mann-Whitney U; for binary metrics use a two-proportion z-test or chi-square. Justify by linking to statistical power, robustness to violations, and business impact.
Pro tip: Mention that at Yelp, where metrics like click-through rates and session durations are common, you'd also consider sequential testing or bootstrapping to handle non-normal data and peeking, showing you understand practical experimentation at scale.
Determine whether the metric is continuous (e.g., revenue, time on page) or binary (e.g., conversion, click). This dictates the family of tests.
Assess normality, variance homogeneity, and sample size. For large samples, t-tests are robust; for small or skewed data, consider non-parametric alternatives.
For continuous metrics: Welch's t-test (unequal variances) or Mann-Whitney U. For binary metrics: two-proportion z-test or chi-square test.
Explain why the test is suitable: e.g., Welch's t-test controls Type I error under unequal variances; non-parametric tests don't assume normality.
Mention adjustments for multiple testing, sequential analysis, or bootstrapping for complex metrics like ratios or percentiles.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the most interesting part of the question.
Start by acknowledging that a non-significant overall result means we cannot confidently attribute the engagement lift to the treatment, so launching fully is premature. Then propose a structured path: validate the test's power and data quality, explore pre-registered subgroups with caution, and decide whether to extend, iterate, or dig deeper based on the business context and risk.
Pro tip: Emphasize that subgroup analysis should be pre-registered or treated as hypothesis-generating, not confirmatory—otherwise you risk p-hacking and false positives. Also, consider the cost of a false launch versus the cost of delaying, and align with stakeholders on the decision threshold.
Check if the test was adequately powered, if there were any data quality issues, and whether the non-significance is due to low sample size or high variance. Calculate the minimum detectable effect and confidence intervals to understand the uncertainty.
A non-significant overall result means we cannot reject the null hypothesis; the observed engagement lift could be due to chance. Avoid over-interpreting the direction of the effect without statistical significance.
If subgroups were pre-registered, examine them with appropriate corrections for multiple comparisons. If not, treat any findings as exploratory and require validation in a follow-up test.
Weigh the potential upside of the engagement lift against the risk of a false positive. Consider the cost of launching, the reversibility, and whether the engagement metric aligns with key business goals.
Based on the above, recommend either extending the test to increase power, running a new test focused on a promising subgroup, or not launching. Avoid fully launching without stronger evidence.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.