This felt like a warmup but I almost tripped on the 'which should you report' part.
Start by defining each metric and explaining how to compute them from a video view count distribution. Then discuss the expected right-skewed shape and the typical mean > median > mode relationship, and finally recommend which metric to report based on the business question and robustness to outliers.
Pro tip: Mention that the mean is sensitive to viral videos, so the median is often more representative for typical performance, but the mean is still useful for total engagement or revenue calculations.
Explain that the mode is the most frequent view count, the median is the middle value when sorted, and the mean is the sum divided by the number of videos.
Note that video view counts typically follow a right-skewed (power-law) distribution with a long tail of highly viewed videos.
State that for right-skewed data, the mean is pulled to the right, so mean > median > mode, and explain why this happens.
Advise that the median is often best for typical performance, while the mean is useful for total impact; the mode is rarely informative for continuous view counts.
Tie the choice of metric to the specific product question, such as measuring typical user engagement versus total watch time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Recognize that the question asks for the probability of at least one success in 10 independent trials with a 10% success rate. Use the complement rule: calculate the probability of no shares (0.9^10) and subtract from 1. Then compute the numerical value and interpret it in a product context.
Pro tip: After computing the probability, briefly discuss how this simple model might be extended to real-world scenarios, such as non-independent sharing behavior or varying share rates, to show depth and business acumen.
Confirm that each video's sharing event is independent and that the probability of sharing is constant at 10% for each video.
Let X be the number of videos shared out of 10. We want P(X ≥ 1).
Compute P(X = 0) = (1 - 0.1)^10 = 0.9^10, then P(X ≥ 1) = 1 - 0.9^10.
Compute 0.9^10 ≈ 0.3487, so P(X ≥ 1) ≈ 0.6513 or 65.13%.
Explain that there is about a 65% chance that at least one video is shared, and discuss implications for virality or engagement metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is the one I wish I'd structured better from the start.
Start by decomposing the metrics and considering possible shifts in user behavior, content mix, or platform changes that could drive watch time up and shares down. Then outline a structured plan to test each hypothesis, using experiments and causal inference methods to distinguish correlation from causation. Finally, discuss how you would validate findings and recommend actions.
Pro tip: Always tie metrics back to the product's north star and consider whether the divergence is actually a problem—sometimes increased watch time with lower shares could indicate a shift to more passive consumption, which might be fine if it aligns with business goals.
Define exactly how watch time and share rate are calculated, and check if the divergence is consistent across segments (e.g., user demographics, content types, platforms).
Brainstorm potential explanations: changes in content recommendation algorithms, seasonal effects, new user influx, UI changes, or shifts in content format (e.g., longer videos).
For each hypothesis, design observational analyses (e.g., cohort analysis, regression) and experiments (e.g., A/B tests) to test the relationship, ensuring to control for confounders.
Use methods like instrumental variables, difference-in-differences, or propensity score matching to establish causality, and validate with experiments where possible.
Summarize findings, quantify the impact, and propose actionable recommendations, considering potential trade-offs and long-term effects.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.