[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/) › Sensitivity

# Sensitivity

- Measure

Of all the people who have the disease, the share the test correctly calls positive.

Updated Nov 5, 2026

## Key points

- Sensitivity = TP / (TP + FN) = a / (a + c): it is calculated within the diseased column.
- A highly sensitive test that comes back negative rules out: it has few false negatives.
- It is preferred for screening and when missing a case is serious (a treatable, contagious or lethal disease).
- False negative rate = 1 − sensitivity.
- In principle it does not depend on prevalence, but it does depend on the spectrum of patients studied.

On this page

- The basics Key points
- What it is
- Formula and reading
- Worked example
- Where sensitivity sits in the 2×2 table
- Markers
- How to read it
- Common pitfalls
- Memory aid

- Study Test yourself

- Connections Connections

## What it is

Sensitivity is a test's ability to pick up people who are ill: the proportion of true positives among everyone who has the disease by the reference standard. A highly sensitive test misses few cases (few false negatives), so a negative result helps rule the disease out. It is the property wanted in a screening test.

## Formula and reading

- Sensitivity = TP / (TP + FN).
- It ranges from 0 to 1 (or 0 to 100%). 100% = no diseased person is missed.
- Reading: a sensitivity of 92% misses 8 of every 100 people with the disease.
- It is linked to specificity through the cutoff: gaining one usually costs the other.

## Worked example

- Data: a rapid test is given to 200 people who have the disease by the reference standard.
- Result: 184 test positive (true positives) and 16 test negative (false negatives).
- Sensitivity = 184 / (184 + 16) = 184 / 200 = 0.92, that is 92%.
- False negative rate = 16 / 200 = 8%.
- Reading: the test detects 92 of every 100 diseased people; a negative makes the disease unlikely, especially when suspicion was low.

## Where sensitivity sits in the 2×2 table

| Test result | Diseased | Healthy |
| --- | --- | --- |
| Positive | TP (a) | FP (b) |
| Negative | FN (c) | TN (d) |
| Column measure | Sensitivity = a/(a+c) | Specificity = d/(b+d) |

## Markers

- Se
- TP
- FN

## How to read it

- Population screening tests aim for high sensitivity so cases are not missed; positives are confirmed afterwards.
- In the emergency department, a negative result on a highly sensitive test can support not pursuing an unlikely cause further.

## Common pitfalls

- Calculating sensitivity with all positives in the denominator (that is the positive predictive value).
- Believing a sensitive test confirms the disease when it is positive.
- Measuring sensitivity only in severe cases, which inflates it (spectrum bias).

## Memory aid

SnNout: a Sensitive test, when Negative, rules the disease out.

## Test yourself

Think of the answer, then tap the question to see it.

## Of 80 diseased people, a test detects 72. What is its sensitivity?

72 / 80 = 0.90, that is 90%.

## Why does a negative result on a highly sensitive test help rule out the disease?

Because it gives few false negatives: almost every diseased person tests positive, so a negative makes the disease unlikely.

## Which property is prioritised in a screening test?

Sensitivity, so that cases are not missed; positives are then confirmed with a more specific test.

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## Connections

## Related 6

- [Specificity](https://durgical.com/en/epidemiology/specificity/)
- [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/)
- [Likelihood ratios](https://durgical.com/en/epidemiology/likelihood-ratios/)
- [Principles of screening](https://durgical.com/en/epidemiology/principles-of-screening/)
- [Spectrum bias](https://durgical.com/en/epidemiology/spectrum-bias/)

[← 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/sensitivity/