This sounds like a warmup but it really isn't.
Start by clarifying the metric definition and user scope, then describe the distribution shape using key statistical properties, and finally discuss factors that drive the shape and how you would validate it with data. Emphasize that daily minutes on Facebook is typically right-skewed with a long tail, not a normal distribution.
Pro tip: Acknowledge that the distribution is likely multimodal due to different user segments (e.g., casual vs. power users) and that median and percentiles are more informative than the mean for skewed data. Mentioning that you would segment by user type or platform shows product sense and analytical maturity.
Define what 'daily minutes spent on Facebook per user' means: is it time spent actively engaging, or including passive scrolling? Specify the user population (e.g., global daily active users) and time frame.
State that the distribution is right-skewed with a long tail: most users spend a moderate amount (e.g., 10-30 minutes), while a small fraction spends hours. It may be multimodal due to different usage patterns.
Discuss that the mean is pulled up by heavy users, so median and percentiles (e.g., 50th, 90th, 99th) better represent typical usage. Mention variance and skewness.
Identify factors that create the shape: user demographics, platform (mobile vs. desktop), feature usage (e.g., video, messaging), and external events (e.g., holidays).
Describe how you would validate the distribution with data (e.g., histogram, log-normal fit) and discuss product implications, such as targeting power users or improving engagement for casual users.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.