Explain Statistical Concepts in A/B Testing and Corrections

Quick Overview

This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Explain Statistical Concepts in A/B Testing and Corrections states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Explain Statistical Concepts in A/B Testing and Corrections

Company: Meta

Role: Data Scientist

Category: Statistics & Math

Difficulty: medium

Interview Round: Onsite

##### Scenario During an experiment review, stakeholders challenge your understanding of statistical validity. ##### Question Define p-value, statistical power, Type I error and Type II error in the context of A/B testing. Why does tracking multiple metrics or variants require corrections such as Bonferroni? Demonstrate with an example. ##### Hints Link definitions to risk of false positives/negatives; show how family-wise error inflates.

Overview: This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Explain Statistical Concepts in A/B Testing and Corrections states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

|Home/Statistics & Math/Meta
Meta logo
Meta
Aug 4, 2025
mediumData ScientistOnsiteStatistics & Math
5
0

Explain Statistical Concepts in A/B Testing and Corrections

A/B Testing: p-values, Power, and Error Rates with Multiple Comparisons

Context

You are reviewing the results of an online A/B experiment. Stakeholders question whether your findings are statistically valid, especially because you track several metrics and may have more than two variants.

Task

  1. Define the following in the context of A/B testing:
    • p-value
    • Type I error
    • Type II error
    • Statistical power
  2. Explain why tracking multiple metrics and/or testing multiple variants inflates false positives and requires corrections (e.g., Bonferroni).
  3. Demonstrate with a concrete numerical example how family-wise error rate (FWER) grows with the number of tests and how Bonferroni controls it.

Clarifying Questions to Ask Guidance

  • Clarify the random variables, distributional assumptions, independence assumptions, and desired output.
  • Show enough derivation for the interviewer to follow the reasoning.
  • Explain how you would validate the result with simulation or sensitivity checks.

What a Strong Answer Covers Guidance

  • A correct setup with definitions, formulas, and boundary conditions.
  • A step-by-step derivation or estimation plan.
  • Interpretation of the result, including uncertainty and practical limitations.
  • Checks for assumptions, edge cases, and numerical stability.

Follow-up Questions Guidance

  • How would the result change if the assumptions were relaxed?
  • Can you verify the answer with a simulation?
  • What is the most likely source of estimation error?
Loading comments...

Browse More Questions

More Statistics & Math•More Meta•More Data Scientist•Meta Data Scientist•Meta Statistics & Math•Data Scientist Statistics & Math