Start by framing the expected distribution shape (likely right-skewed, heavy-tailed) based on typical P2P usage patterns, then estimate the relative positions of mean, median, mode, and 95th percentile. Finally, explain how and why the distribution shifts after two months, considering network effects, habit formation, and product changes.
Pro tip: Anchor your answer in a concrete mental model: most users make 0-2 transfers, a small group makes many, so the mean is pulled above the median. Acknowledge that the exact numbers depend on factors like incentives and social graph, showing you think like a data scientist.
Explain that P2P transfers are typically right-skewed with a long tail: many users make few or zero transfers, while a minority make many. This is common for new social features where adoption is uneven.
Provide rough estimates: mode at 0 or 1, median around 1-2, mean higher (e.g., 3-5) due to heavy users, and 95th percentile around 10-15. Emphasize that these are illustrative and depend on context.
Describe how the distribution might become less skewed: more users adopt, median and mode may increase, mean may rise but at a slower rate, and the 95th percentile could grow if power users intensify usage.
Identify reasons: network effects (more friends join), habit formation, product improvements, marketing, and seasonal factors. Also note potential saturation or churn effects.
Mention that actual distribution depends on specific product design, target market, and incentives. Suggest validating with real data and considering segmentation (e.g., by user type).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.