How do I calculate sensitivity and specificity values?Īn ideal test rarely overlooks the thing you are looking for (i.e., it is sensitive) and rarely mistakes it for something else (i.e. SpPin: A test with a high specificity value ( Sp) that, when positive ( P) helps to rule in a disease ( in). SnNout: A test with a high sensitivity value ( Sn) that, when negative ( N), helps to rule out a disease ( out). SnNouts and SpPins is a mnemonic to help you remember the difference between sensitivity and specificity. Also referred to as type I errors, false positives are the rejection of a true null hypothesis (the null hypothesis being that the sample is negative). Another test that incorrectly identifies 30 % of healthy people as having the condition would be deemed to be less specific, having a higher false positive rate (FPR). For example, a test that identifies all healthy people as being negative for a particular illness is very specific. The specificity of a test, also referred to as the true negative rate (TNR), is the proportion of samples that are genuinely negative that give a negative result using the test in question. Also referred to as type II errors, false negatives are the failure to reject a false null hypothesis (the null hypothesis being that the sample is negative). Another test that only detects 60 % of the positive samples in the panel would be deemed to have lower sensitivity as it is missing positives and giving higher a false negative rate (FNR). For example, a test that correctly identifies all positive samples in a panel is very sensitive. The sensitivity of a test is also called the true positive rate (TPR) and is the proportion of samples that are genuinely positive that give a positive result using the test in question. By using samples of known disease status, values such as sensitivity and specificity can be calculated that allow you to evaluate just that. When developing diagnostic tests or evaluating results, it is important to understand how reliable those tests and therefore the results you are obtaining are.

Specificity principle