A true negative occurs when a test correctly identifies the absence of a condition. That is, the individual does not have the condition, and the test appropriately returns a negative result. 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).
True negatives contribute to several important accuracy measures, including specificity, negative predictive value (NPV), and overall accuracy. These metrics help evaluate how reliably a test avoids false alarms and confirms the absence of a condition.
For example, if a tuberculosis test correctly returns a negative result for someone who does not have the disease, that person avoids unnecessary anxiety, treatment, and follow-up testing. This outcome builds confidence in the test’s ability to rule out the condition.
