What specificity measures
Specificity, also called the true negative rate, tells you how well a test correctly identifies people who do not have a condition. It answers the question: of everyone who is genuinely healthy (disease-free), what fraction did the test correctly clear? A highly specific test rarely raises a false alarm in healthy people.
The formula
Specificity is computed from two cells of a 2×2 confusion matrix:
Specificity = TN / (TN + FP)
- TN (true negatives) — people without the condition who correctly test negative.
- FP (false positives) — people without the condition who incorrectly test positive (a false alarm).
The denominator TN + FP is the total number of people who truly do not have the condition. Specificity ranges from 0 to 1 (0% to 100%). It ignores the sick group entirely — the true positives and false negatives play no part in the specificity calculation.
Worked example
Suppose a screening test is run on 100 people who are known to be disease-free. It correctly returns a negative result for 80 of them (TN = 80) but incorrectly flags 20 as positive (FP = 20). Then specificity = 80 / (80 + 20) = 80 / 100 = 0.80, or 80%. Equivalently, the false positive rate is 1 − 0.80 = 0.20, meaning 20% of healthy people are wrongly flagged.
Specificity versus sensitivity
Specificity is the partner of sensitivity (the true positive rate, TP / (TP + FN)). Sensitivity measures how well a test catches the people who do have the condition; specificity measures how well it clears the people who do not. The two are computed from different halves of the confusion matrix, so a test can be strong in one and weak in the other. Both are usually reported together because neither alone describes a test's performance. A useful memory aid: SpPin — a highly Specific test, when Positive, rules in the disease.
Common reference points
- A specificity of 1.0 (100%) means zero false positives — every healthy person tests negative.
- Confirmatory tests are deliberately built for high specificity (often >99%) so that a positive result is trustworthy and few healthy people are wrongly alarmed.
- Broad screening tests often sacrifice some specificity to gain sensitivity, accepting more false positives so that fewer true cases are missed; positives are then re-checked with a more specific confirmatory test.