Mock Interview STAT

Author

Cecilia Regueira

Top 10 Statistics Interview Questions

Note

💡 QA

This document presents the top 10 statistics questions that most frequently come up in job interviews. These questions are based on interviews I have personally participated in, either as a candidate or as an interviewer when assessing new hires. The goal is to highlight the statistical concepts that employers commonly expect candidates to understand.

  1. What is the difference between descriptive and inferential statistics?
    Give examples of when you would use each.

  2. Can you explain the difference between correlation and causation?
    How would you test whether a relationship is causal?

  3. What is a p-value, and how should it be interpreted?
    What are common misconceptions about p-values?

  4. Explain the difference between Type I and Type II errors.
    In what situations might one be more costly than the other?

  5. What assumptions are required for linear regression?
    How would you check whether these assumptions are violated?

  6. What is the Central Limit Theorem and why is it important?
    How does it apply in real-world data analysis?

  7. What is a confidence interval and how do you interpret it?
    Explain what it means in practice and what a 95% confidence interval represents.

  8. What is overfitting, and how can it be prevented?
    How does the bias–variance tradeoff relate to this?

  9. How would you handle missing data in a dataset?
    What are the pros and cons of different approaches?

  10. How do you evaluate the performance of a statistical or predictive model?
    Which metrics would you choose and why?

  11. What happens to a confidence interval when the sample size doubles?
    Explain how increasing the sample size affects the width of the confidence interval and why.

Bonus

Question 1: Statistics Foundations

You are analyzing ride data for Uber.

  • The probability that a user takes a ride in the morning rush hour is 10%.
  • The probability that a user takes a ride in the evening rush hour is 20%.
  • The probability that a user takes an evening rush hour ride given that they took a morning rush hour ride is 50%.

If we observe that a user took an evening rush hour ride, what is the probability that they also took a morning rush hour ride?

Question 2: Experiment Design & Power Analysis

When designing an experiment, given a fixed sample size, significance level, and desired power, we compute a Minimum Detectable Effect (MDE).

Suppose you run the experiment, collect the data, and observe an effect size that is smaller than the MDE, yet the result is statistically significant.

How is this possible? Shouldn’t effects smaller than the MDE be non‑significant?

Question 3: Correlation vs. Causation

Amazon Prime members place an average of 10 orders per month, while non‑Prime users place 2 orders per month.

Does Amazon Prime membership cause users to place more orders? Explain your reasoning.

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