Explain Type I/II errors vs precision/recall
Company: ByteDance
Role: Data Scientist
Category: Statistics & Math
Difficulty: medium
Interview Round: Technical Screen
In the context of binary classification and hypothesis testing:
1) Define Type I error and Type II error.
2) Explain how they relate to false positives/false negatives and to precision/recall.
3) In what situations is a Type I error more costly than a Type II error, and vice versa? Give concrete examples.
4) Given a scenario, be able to identify which mistake corresponds to Type I vs Type II (e.g., fraud detection, spam filtering, medical screening).
Overview: This question evaluates understanding of Type I and Type II errors in hypothesis testing and binary classification, their correspondence to false positives and false negatives, and how those concepts relate to precision and recall within the Statistics & Math domain.