I went straight for a paired t-test comparing first video duration vs.
Frame the analysis as a within-user comparison: for each user, compare the duration of their first upload to the duration of their later uploads. Define the metric clearly (e.g., average duration of later uploads minus first upload duration), then use a paired statistical test (like a paired t-test or Wilcoxon signed-rank test) on the per-user differences. Finally, write SQL to extract the necessary data and compute the test statistic.
Pro tip: Mention that you would check for confounding factors like video category or user tenure, and consider segmenting by user type (e.g., casual vs. professional creators) to see if the effect holds across groups.
Select users who have at least two uploads (so we can compare first vs. later). Decide on a time window (e.g., first 30 days after signup) and exclude outliers like videos with zero duration or extreme lengths.
For each user, compute the duration of their first upload and the average duration of their subsequent uploads (or median if skewed). The metric of interest is the difference: later_avg_duration - first_duration.
Use a paired t-test if differences are normally distributed; otherwise, use the Wilcoxon signed-rank test. Report the mean difference, confidence interval, and p-value.
Write a query to extract user_id, video_id, upload_timestamp, and duration, then use window functions to rank uploads per user. Compute first vs. later durations and aggregate per user.
Check if the difference is statistically and practically significant. Consider segmenting by user activity level or video category to ensure robustness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.