False negative

A false negative is a test result that indicates a condition is absent when it is actually present. Its defining feature is disagreement between the negative result and the true state.

Sensitivity and specificity: Sensitivity is the share of affected cases a test detects; specificity is the share of unaffected cases it correctly excludes. Low sensitivity produces more false negatives among people who have the condition.
Negative predictive value: Negative predictive value is the probability that a person with a negative test result truly lacks the condition. False negatives reduce the reliability of a negative result, especially when the condition is common.
Medical screening: Medical screening tests people without symptoms to identify those who may have a disease or risk factor. Screening programs weigh missed cases against the costs and harms of follow-up testing.
False positive: A false positive is a test result indicating that a condition is present when it is actually absent. It is the opposite classification error: a false alarm rather than a missed condition.
Binary classification: Binary classification assigns each case to one of two classes, such as positive or negative. False negatives arise when a binary test assigns an affected case to the negative class.
Detection threshold: A detection threshold is the boundary above or below which a measurement is classified as positive. A condition can be present yet remain below the test’s positive cutoff.
Diagnostic delay: Diagnostic delay is the time between the onset or detectability of a condition and its diagnosis. A false negative can postpone further evaluation and recognition of disease.
Mammography: Mammography uses low-dose X-rays to examine breast tissue, commonly for breast cancer screening and diagnosis. Dense breast tissue and other factors can hide cancers from a screening image.
True negative: A true negative is a negative test result when the condition is genuinely absent. Unlike a false negative, this result correctly identifies absence.
Ground truth: Ground truth is the best available determination of a case’s actual state, used to assess predictions or measurements. A result can be called false negative only by comparison with the actual condition.

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