A measure of a screening, diagnostic, monitoring or other lab test’s accuracy in terms of the true negative rate — that is, the number of people with a negative test result who truly are negative. High specificity means a higher likelihood that a positive result really is a true positive.
Deeper dive
Specificity and sensitivity both refer to different characteristics of screening or diagnostic tests. They refer to the likelihood that a positive or negative result truly is an accurate positive or negative result. Sensitivity refers to the proportion of people who have a disease and also test positive, so it tells you the true positive rate — that is, a positive result when the person truly is positive. A very highly sensitive test for a disease resulting in a negative result means it’s highly likely that the person does not have the disease. It’s reported as a ratio: true positives/(true positive false negative).
Specificity refers to the opposite — the proportion of people without a disease who test negative, or the true negative rate. A positive result with a very high specificity therefore means it’s more likely that the person does have the disease. Specificity is also reported as a ratio: true negatives/(true negative false positives).
Where things get tricky is in understanding how these relate to false positives and false negatives. With most tests, it’s difficult to achieve a high specificity and a high sensitivity. Generally speaking, the higher the sensitivity is, the lower the specificity is, and vice versa. (When both are high, it’s an extremely accurate test.) A test with a sensitivity of 100% means it correctly identifies everyone who is positive, but it also picks up more people who are not truly positive. A test with a sensitivity of 75% means it correctly identifies 75% of people who take it as positive, but 25% of people’s disease goes undetected with false negatives. The risk of a screening test with a very high sensitivity is a higher risk of a false positive.
Because specificity does the opposite, it has a higher risk of missing people who *do* have the disease. A specificity of 100% correctly identifies everyone who does not have a disease — along with more or false negatives. A specificity of 80% correctly identifies 80% of the people who do not have the disease, but it does not pick up the 20% who receive false positives.
If your head is spinning a bit, you’re not alone. It can be difficult to wrap your head around these concepts, and it generally becomes easier with more familiarity and time. Reading specific examples can also be helpful.