# False Positives and False Negatives

In addition to the Statistical Results components, it is also important to understand how they affect False Positive and False Negative results.

Assume that there is an image inspection system that is built for capturing defects in images. If it captures one or more defects in an image, let's say the inspection result for that image is Positive, and if it captures no defect at all, the inspection result for that image is Negative. Then, the statistical results of the inspection task can be summarized into the followings:

- False Positive (also known as a Type I Error)

  - An inspection system identifies a class of a character feature, but this character feature does not actually belong to that class.

  - For Red Analyze, an inspection system identifies a view or a pixel defective, but this view or pixel does not actually have a defect.

- False Negative (also known as a Type II Error)

  - An inspection system could not identify a class of a character feature, but this character feature should have been identified as belonging to that class.

  - For Red Analyze, an inspection system could not identify a view or a pixel defective, but this view or pixel should have been identified defective.