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This is a medium difficulty Statistics & Math question, commonly asked during Onsite rounds at Meta.

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This question is commonly asked for Data Scientist candidates at Meta during technical interviews.

Explain Type I vs. Type II Errors in A/B Testing

Last updated: Mar 29, 2026

Quick Overview

This question evaluates understanding of statistical hypothesis testing—specifically the concepts of Type I and Type II errors, power and threshold selection—and competency in robust estimation and inference for skewed, heavy-tailed A/B testing metrics.

A/B Testing Errors and Estimation Under Skewed Metrics

Context

You are analyzing an A/B experiment for a product feature. You need to explain the statistical error types and defend thresholds you would use. The primary metric can be heavy-tailed (e.g., time spent, revenue per user) with outliers.

Tasks

Define and contrast Type I and Type II errors in A/B testing, and explain how you would choose acceptable levels (α and β/power) in this context.
If the metric distribution is highly skewed with outliers, describe how you would estimate the treatment lift and construct a confidence interval. Discuss appropriate methods (e.g., non-parametric tests, bootstrapping, or transformations) and when you would use them.

Solution

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