A computer vision model is trained to identify cats on a training set that is...

→ A Type II error occurs when the model fails to identify a positive instance — in this case, a cat. That is, it incorrectly classifies a cat (positive class) as a dog (negative class). This is also referred to as a false negative.

Why the other options are incorrect:

A: “Error due to reality” is not a recognized statistical concept.

B: A false positive would mean misclassifying a dog as a cat (opposite error).

C: Sampling error refers to discrepancies between the sample and population, not a misclassification.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 1.5:“Type II errors occur when a model incorrectly identifies a true positive as a negative — also known as a false negative.”

Pattern Recognition and Machine Learning, Chapter 9:“In binary classification, a Type II error means failing to detect a positive class instance, leading to a false negative result.”