A false negative occurs when a test or model fails to identify a case where the condition or outcome is actually present. It is one of the four mutually exclusive outcomes in a confusion matrix, which categorizes test results into: true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN).
False negatives directly affect measures like sensitivity, negative predictive value (NPV), and false negative rate (FNR). These metrics help determine how often the test fails to detect a condition that is truly present—an especially serious issue in high-stakes settings like medicine or safety.
For example, a false negative result on a mammogram means a person with breast cancer is told they don’t have it. This can delay treatment, allow the disease to progress, and significantly reduce the chance of a successful outcome.
