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Compare queueing systems and common distributions

Last updated: Jun 15, 2026

Quick Overview

LinkedIn data scientist technical-screen statistics question. It evaluates queueing-theory pooling (one shared queue vs. separate queues, compared on expected wait, variance, and fairness), normal vs. mixture distributions for U.S. heights, the right-skewed heavy-tailed distribution of social-network connections with the mode < median < mean ordering, and why L1 (lasso) and L2 (ridge) estimators are biased via the bias–variance tradeoff.

  • medium
  • LinkedIn
  • Statistics & Math
  • Data Scientist

Compare queueing systems and common distributions

Company: LinkedIn

Role: Data Scientist

Category: Statistics & Math

Difficulty: medium

Interview Round: Technical Screen

##### Question You are asked a series of statistics fundamentals questions in a data science technical screen. 1. **Queueing.** A bank with 5 tellers can be organized two ways: - **System A:** all 5 tellers share **one common queue**. - **System B:** each teller has **their own separate queue**. Assume customer arrivals are random, tellers are similarly (but not necessarily identically) skilled, customers are served first-come-first-served within a line, and some customers take much longer than others. As a customer, which system would you rather join, and why? Compare the two in terms of **expected waiting time, variance of waiting time, and fairness.** 2. **Height distributions.** Sketch or describe the distribution of **adult male heights in the United States** and, separately, the distribution of **adult female heights in the United States.** 3. **Pooled distribution.** If you pool men and women together and ignore sex, what does the **combined height distribution** look like? 4. **Network degree.** On a social network such as LinkedIn, describe the distribution of the **number of connections per user.** Is it symmetric, left-skewed, or right-skewed? 5. **Summary statistics.** For that connections distribution, how do the **mean, median, and mode** compare, and why? If asked for the likely scale of the mean, what factors would determine it? 6. **Regularization bias.** Are the **L1-regularized (lasso)** and **L2-regularized (ridge)** estimators unbiased? Why or why not?

Quick Answer: LinkedIn data scientist technical-screen statistics question. It evaluates queueing-theory pooling (one shared queue vs. separate queues, compared on expected wait, variance, and fairness), normal vs. mixture distributions for U.S. heights, the right-skewed heavy-tailed distribution of social-network connections with the mode < median < mean ordering, and why L1 (lasso) and L2 (ridge) estimators are biased via the bias–variance tradeoff.

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LinkedIn logo
LinkedIn
Jul 8, 2025, 12:00 AM
Data Scientist
Technical Screen
Statistics & Math
3
0
Question

You are asked a series of statistics fundamentals questions in a data science technical screen.

  1. Queueing. A bank with 5 tellers can be organized two ways:
    • System A: all 5 tellers share one common queue .
    • System B: each teller has their own separate queue .
    Assume customer arrivals are random, tellers are similarly (but not necessarily identically) skilled, customers are served first-come-first-served within a line, and some customers take much longer than others. As a customer, which system would you rather join, and why? Compare the two in terms of expected waiting time, variance of waiting time, and fairness.
  2. Height distributions. Sketch or describe the distribution of adult male heights in the United States and, separately, the distribution of adult female heights in the United States.
  3. Pooled distribution. If you pool men and women together and ignore sex, what does the combined height distribution look like?
  4. Network degree. On a social network such as LinkedIn, describe the distribution of the number of connections per user. Is it symmetric, left-skewed, or right-skewed?
  5. Summary statistics. For that connections distribution, how do the mean, median, and mode compare, and why? If asked for the likely scale of the mean, what factors would determine it?
  6. Regularization bias. Are the L1-regularized (lasso) and L2-regularized (ridge) estimators unbiased? Why or why not?

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