# False Positive and False Negative Results

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

A False Positive (also known as a Type I Error) is when an inspection system rejects a part that does not have a defect.

A False Negative (also known as a Type II Error) is when an inspection system passes a part that should have been classified as a failure.

These affect the VisionPro Deep Learning statistical results data in the following ways:

- Recall - A neural network with a poor Recall percentage typically fails to predict correctly, and can return False Negative judgments.
- Precision - A neural network with a poor Precision percentage typically predicts incorrectly, and can result in False Positive judgments.