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

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

Explain Type I and Type II Errors in Hypothesis Testing

Last updated: Mar 29, 2026

Quick Overview

This question evaluates understanding of hypothesis testing fundamentals, specifically recognition of Type I versus Type II errors, the distinction between false positives and false negatives, and the ability to articulate trade-offs in experimental design.

OneMain Financial

Jul 12, 2025, 6:59 PM

Data Scientist

Onsite

Statistics & Math

Statistics Fundamentals: Type I vs Type II Errors and A/B Test Trade-offs

Task

You are discussing hypothesis testing in the context of a past modeling or experimentation project.

Define Type I error and Type II error in hypothesis testing.
Explain the difference between them.
Provide one real-world example for each type of error.
Describe how you would manage the trade-off between the two in an A/B test.

Hints: Relate to false positives vs. false negatives; discuss significance level (alpha), statistical power (1 − beta), and practical business costs.

Solution

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