is the proportion of people with a negative test who are free of disease. See also SpPins and SnNouts.

For a diagnostic procedure, the conditional probability of absence of disease given a negative test result.

The likelihood that an individual with a negative test result is actually unaffected and/or does not have the particular gene mutation in question

The probability that a test-negative individual is truly free of infection.

Proportion of people with a negative test who are free of the target disorder. See also likelihood ratio.

(PV-): The probability that the condition of interest is false if the result is negative – for example, the probability that the disease is absent given a negative test result.

The probability that patients who have tested negative to a disease will not have it. A high negative predictive value indicates that there is a high probability that the patient who has a negative test result probably does not have the disease in question.

is the proportion of people with a negative test who are free of disease. See also Calculating Sensitivity and Specificity.

The probability that a subject does not have the disease when the test result is negative. Synonyms include predictive value negative. Negative predictive value = d/m2 = TN/(TN+FN). By application of Bayes' Rule, the negative predictive value also can be defined as a function of the pretest probability of disease (p), sensitivity, and specificity: negative predictive value = [(1-p) . specificity]/[(1-p) . specificity + p . (1- sensitivity)].

The negative predictive value is the proportion of patients with negative test results who are correctly diagnosed.