Characterize metric distribution and quantiles

Quick Overview

This question evaluates a data scientist's competency in descriptive statistics and robust metric analysis within the Statistics & Math domain, focusing on distribution characterization, empirical quantiles, measures of central tendency, and the effects of outlier handling on variance and experimental power.

Characterize metric distribution and quantiles

Company: Meta

Role: Data Scientist

Category: Statistics & Math

Difficulty: medium

Interview Round: Onsite

Your chosen KPI is per-video watch time (seconds). Based on the following 20-sample dataset collected from a pilot: [3, 5, 6, 6, 7, 8, 9, 10, 12, 15, 16, 20, 24, 30, 35, 45, 60, 90, 120, 180]. Tasks: (1) Describe the likely distribution shape (skewness/heaviness of tail) and sketch it verbally. (2) Compute the sample median, mode, and the 95th percentile using the linear interpolation method for empirical quantiles; show steps. (3) Compare mean vs median for this data and argue which is the more decision‑robust location estimator and why. (4) If we winsorize at the 99th percentile, explain qualitatively how that would change variance and statistical power in an A/B test on watch time.

Quick Answer: This question evaluates a data scientist's competency in descriptive statistics and robust metric analysis within the Statistics & Math domain, focusing on distribution characterization, empirical quantiles, measures of central tendency, and the effects of outlier handling on variance and experimental power.

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Oct 13, 2025, 9:49 PM
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KPI Analysis: Per-Video Watch Time (seconds)

You are evaluating a pilot dataset for the KPI "per‑video watch time" (in seconds). The dataset (n = 20) is sorted ascending:

3, 5, 6, 6, 7, 8, 9, 10, 12, 15, 16, 20, 24, 30, 35, 45, 60, 90, 120, 180

Tasks

  1. Distribution shape
    • Describe the likely distribution shape (skewness and tail heaviness) and sketch it verbally.
  2. Summary statistics with interpolation
    • Compute the sample median, mode, and the 95th percentile using the linear interpolation method for empirical quantiles. Show steps.
  3. Mean vs. median
    • Compare the mean versus the median for this data and argue which is the more decision‑robust location estimator and why.
  4. Winsorization and power
    • If you winsorize at the 99th percentile, explain qualitatively how that would change variance and statistical power in an A/B test on watch time.
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