Intermediate interview questions in Analytics & Metrics, page 4
Average latency up 50ms but p99 flat: diagnose the discrepancy
Tests if you know mean reflects full distribution while p99 is a threshold. Strong answers hypothesize body shift like cache misses or traffic mix changes, and demand histograms and segmentation by endpoint. Red flag: blaming outliers, which would raise p99.
Average latency is up 50ms, but p99 is flat. How do you diagnose this?
Tests your grasp of latency metrics. A rising average with flat p99 means the *bulk* of requests (p50-p90) slowed, not the tail. Hypothesize a common bottleneck and segment data by endpoint/user to find it. Red flag: blaming new, slow outliers.
Average latency is up, but p99 is flat. Why?
This tests your grasp of latency distributions. Hypothesize that a large group of typical requests slowed, pulling up the average but not crossing the p99 threshold. Segment by endpoint or customer to find the cohort.
Write SQL to generate a monthly cohort retention table from raw events
Tests window functions and date truncation for cohort analysis. A strong answer finds each user's first month, counts returning users per period, and divides by cohort size. Aggregating all users without isolating acquisition month hides new-user churn.
How to query a monthly cohort retention table in SQL?
Tests your ability to translate a core business metric into a multi-step SQL query. A good answer finds each user's acquisition month, joins that back to their activity, and pivots the data into a cohort grid. A red flag is calculating aggregate retention.
Generate a monthly cohort retention table from user events
This tests your ability to translate a business metric into a multi-step SQL query. A great answer defines cohorts by first activity, maps subsequent activity to period indices, counts distinct users, and pivots the result.
A/B test shows 5% lift with imbalanced mobile traffic. What next?
This tests sample ratio mismatch and Simpson's paradox. A strong answer says the lift is confounded by platform, demands stratified analysis to isolate the true effect, and checks randomization. A red flag is accepting the 5% lift without questioning skew.
A/B Test Results with Skewed Traffic: What's Next?
This tests your ability to spot confounding variables. A good answer invalidates the results due to sampling bias, proposes segmenting the data by device to find the true effect, and suggests re-running the test with correct randomization.
An A/B test has imbalanced traffic. What do you do?
This tests your ability to spot confounding variables and Simpson's Paradox. A good answer first invalidates the aggregate result, then proposes segmenting by device to salvage insights, and finally investigates the root cause.
A/B test p-value 0.08, PM wants to ship. How do you advise?
Tests statistical rigor versus business pragmatism. A strong answer covers pre-registered thresholds, false positive risk, statistical power, confidence intervals, and the business cost of being wrong. Red flag: shipping without quantifying downside risk.
A/B test p-value is 0.08, PM wants to ship. What now?
Tests if you can translate statistical risk into business terms for a PM. A good answer defines the 8% false positive risk, weighs it against the cost of shipping, and suggests next steps like running the test longer instead of just saying no.
p-value is 0.08, significance is 0.05. Ship it?
This tests your ability to translate statistical risk for business partners. Explain that p=0.08 means an 8% chance of a false positive, quantify the cost of a bad decision, and suggest extending the test to increase power.
Explain the difference between correlation and causation with a software example.
Tests whether you distinguish association from causation to avoid blaming production issues. A strong answer defines both concepts, names a confounding variable, and gives a software example with a common cause. Red flag: claiming correlation is causation.
Explain correlation vs. causation with a software example
This tests your critical thinking about data and ability to avoid logical fallacies. A good answer defines both terms, then gives a software example where a third, confounding variable (like traffic) is the true cause of two correlated metrics.
Explain the difference between correlation and causation
Tests if you can avoid statistical fallacies. First, define correlation (association) and causation (cause-effect). Then, explain the difference via a confounding variable. A red flag is giving an example where one metric actually could cause the other.
Determine if a 10% DAU drop is statistically significant
Tests signal vs noise in stable metrics. Good answers define a null hypothesis, compute a test statistic from historical variance, compare to a critical value at set alpha, and check seasonality. Red flag: calling a large drop real without baseline variance.
DAU dropped 10% overnight. Is this a significant change?
Tests your use of statistical hypothesis testing on business metrics. Outline the process: state a null hypothesis (no change), choose a Z-test, calculate the p-value, and compare to an alpha of 0.05. A red flag is guessing causes before proving significance.
How to Statistically Test a 10% DAU Drop?
Tests your knowledge of hypothesis testing. A good answer outlines the steps: state a null hypothesis, choose a test (e.g., Z-test), calculate a p-value, and compare it to a significance level (alpha).
Explain Simpson's Paradox and construct a user engagement scenario
Tests whether you spot trends reversing when population mixes differ. Good answers define the paradox, give a numerical example with per-segment wins but aggregate loss, and warn against segment-only decisions.
Explain Simpson's Paradox with a user engagement example
Tests if you see beyond aggregate data. Define the paradox, give a numerical example where a feature fails overall but wins in segments (e.g., new vs. returning users), and name the confounding variable. A vague definition without numbers is a red flag.
We are hiring for this. Every open role lists the topics its interview covers, so you can prepare for the real thing rather than guessing.
See open roles