I started talking about a right-skewed distribution pretty quickly, which felt right, but then I kind of stumbled when thinking about how to actually characterize it precisely.
Start by clarifying that the distribution is typically heavy-tailed and right-skewed, with most users making few queries and a small fraction making many. Then discuss how you would model it (e.g., log-normal or power law) and validate with data, noting that the exact shape depends on user segmentation and time period.
Pro tip: Acknowledge that the distribution is not purely power law but often log-normal in practice, and mention that outliers (power users) can dominate aggregate metrics, so median and percentiles are more informative than the mean.
Define what 'daily search queries per user' means: is it per active user or all users? Over what time window? This sets the context for the distribution.
Explain that the distribution is right-skewed and heavy-tailed: most users have low query counts (e.g., 0-5), while a small minority have very high counts (100+).
Suggest that a log-normal or power-law distribution often fits such data, and mention that you would test goodness-of-fit to determine the best model.
Highlight that the mean is pulled up by outliers, so median and percentiles (e.g., 90th, 99th) better represent typical usage. This affects product decisions and infrastructure planning.
Note that the distribution may vary by user segment (e.g., new vs. power users) and that you would validate assumptions with real data, checking for multimodality or changes over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.