Specificity
- Measure
Of all the healthy people, the share the test correctly calls negative.
What it is
Specificity is a test's ability to recognise healthy people: the proportion of true negatives among everyone who does not have the disease. A highly specific test gives few false positives, so a positive result helps confirm the disease. It is the property wanted in a confirmatory test, especially when a false positive leads to risky treatment or procedures.
Formula and reading
- Specificity = TN / (TN + FP).
- It ranges from 0 to 1 (or 0 to 100%). 100% = no healthy person tests positive.
- Reading: a specificity of 94% gives 6 false positives per 100 healthy people tested.
- 1 − specificity is the horizontal axis of the ROC curve.
Worked example
- Data: the same test is given to 500 people without the disease by the reference standard.
- Result: 470 test negative (true negatives) and 30 test positive (false positives).
- Specificity = 470 / (470 + 30) = 470 / 500 = 0.94, that is 94%.
- False positive rate = 30 / 500 = 6%.
- Reading: 6 in 100 healthy people would get a false positive; a positive on this test supports the diagnosis, especially when suspicion was high.
Sensitivity versus specificity
| Feature | Sensitivity | Specificity |
|---|---|---|
| Formula | TP / (TP + FN) | TN / (TN + FP) |
| Table column | Diseased | Healthy |
| Error it minimises | False negatives | False positives |
| Most informative result | Negative (rules out) | Positive (rules in) |
| Typical use | Screening | Confirmation |
Markers
- Sp
- TN
- FP
How to read it
- A positive HIV or syphilis screening result is confirmed with a more specific test before the diagnosis is given.
- In rare diseases, even a high specificity produces many false positives in absolute numbers.
Common pitfalls
- Calculating specificity with all negatives in the denominator (that is the negative predictive value).
- Believing a specific test rules out the disease when it is negative.
- Ignoring that in very healthy populations false positives can outnumber true positives.
Test yourself
Think of the answer, then tap the question to see it.
Of 300 healthy people, 285 test negative. What is the specificity?
285 / 300 = 0.95, that is 95%.
Why does a positive result on a highly specific test help confirm the disease?
Because almost no healthy person tests positive, so a positive probably belongs to someone with the disease.
If specificity is 90%, how many false positives are expected per 1,000 healthy people?
100, because the false positive rate is 1 − 0.90 = 10%.
Connections
Educational content on epidemiological and statistical methods; the examples use made-up numbers to teach the calculation. Not a clinical or legal guide: current Colombian rules are in the official sources.
Sources: Sensitivity, Specificity, and Predictive Values: Foundations, Pliabilities, and Pitfalls in Research and Practice (Frontiers in Public Health, 2017); Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests - Guidance for Industry and FDA Staff (U.S. Food and Drug Administration, 2007).
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Representative model, for educational purposes only.