Straightforward on the surface but I almost forgot to handle the NULLs in survey_score.
Start by clarifying the definition of response rate: typically the number of survey responses divided by the number of survey invitations (or eligible users). Then write SQL that counts distinct responses and distinct invitations, ensuring you handle duplicates and filter for the relevant time period. Finally, compute the ratio and consider any necessary joins or subqueries to get accurate counts.
Pro tip: Always confirm with the interviewer whether the denominator should be all invited users or only those who were eligible to respond (e.g., active users). This shows you understand that metric definitions can vary and impact business decisions.
Ask the interviewer to define what constitutes a 'response' and an 'invitation' (or eligible user). Confirm whether the rate is based on unique users or total events.
Determine which columns indicate survey events (e.g., event_type, user_id, survey_id, timestamp) and whether invitations and responses are in the same table or separate tables.
Use COUNT(DISTINCT user_id) for responses and invitations, applying filters for event types and time ranges. Handle NULLs and duplicates appropriately.
Divide the response count by the invitation count, using CAST to float to avoid integer division. Optionally multiply by 100 for a percentage.
Mention potential issues like users who respond without an invitation, multiple responses per user, or time zone considerations, and how you would handle them.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by writing a SQL query that computes average survey scores for new and existing users, then use a statistical test (e.g., t-test) to assess significance. Explain how you would handle data quality issues and interpret the results in a product context.
Pro tip: Mention that you would check for practical significance alongside statistical significance, and consider segmenting by user cohorts or time periods to uncover deeper insights.
Clearly define what constitutes a 'new' vs 'existing' user (e.g., based on signup date or first activity) and identify the survey score metric (e.g., average rating).
Use SQL to calculate the average survey score and sample size for each group, ensuring proper filtering and grouping.
Choose an appropriate test (e.g., two-sample t-test) to compare means, and compute the p-value or confidence interval, either in SQL or by exporting results to Python/R.
Assess statistical significance, check assumptions (e.g., normality, variance), and consider practical significance and potential confounders.
Summarize results clearly, including effect size and business implications, and suggest next steps if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.