[Durgical](https://durgical.com/en/) › [DurgiPedia](https://durgical.com/en/durgipedia/) › [Epidemiology](https://durgical.com/en/epidemiology/) › [Diagnostic tests](https://durgical.com/en/epidemiology/areas/diagnostic/) › Specificity

# Specificity

- Measure

Of all the healthy people, the share the test correctly calls negative.

Updated Nov 5, 2026

## Key points

- Specificity = TN / (TN + FP) = d / (b + d): it is calculated within the healthy column.
- A highly specific test that comes back positive rules in: it has few false positives.
- It is preferred for confirmation and when a false positive is harmful (surgery, toxic treatment, stigma).
- False positive rate = 1 − specificity.
- A sensitive test usually screens and a specific test confirms.

On this page

- The basics Key points
- What it is
- Formula and reading
- Worked example
- Sensitivity versus specificity
- Markers
- How to read it
- Common pitfalls
- Memory aid

- Study Test yourself

- Connections Connections

## 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.

## Memory aid

SpPin: a Specific test, when Positive, rules the disease in.

## 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%.

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## Connections

## Related 5

- [Sensitivity](https://durgical.com/en/epidemiology/sensitivity/)
- [Positive and negative predictive values](https://durgical.com/en/epidemiology/positive-and-negative-predictive-values/)
- [Choosing a cutoff: sensitivity vs specificity](https://durgical.com/en/epidemiology/choosing-a-cutoff-sensitivity-vs-specificity/)
- [Serial and parallel testing](https://durgical.com/en/epidemiology/serial-and-parallel-testing/)
- [ROC curve](https://durgical.com/en/epidemiology/roc-curve/)

[← All of epidemiology](https://durgical.com/en/epidemiology/)

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](https://www.frontiersin.org/articles/10.3389/fpubh.2017.00307/full) (Frontiers in Public Health, 2017); [Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests - Guidance for Industry and FDA Staff](https://www.fda.gov/regulatory-information/search-fda-guidance-documents/statistical-guidance-reporting-results-studies-evaluating-diagnostic-tests-guidance-industry-and-fda) (U.S. Food and Drug Administration, 2007).

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Representative model, for educational purposes only.

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License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Cite as: DurgiPedia (durgical.com), https://durgical.com/en/epidemiology/specificity/