Specificity As both sensitivity and specificity are proportions, their confidence intervals can be computed using the standard methods for proportions2. SnNout: A test with a high sensitivity value (Sn) that, when negative (N), helps to rule out a disease (out). In some cases, the purpose of the test is to confirm the diagnosis, but some testing is also used more widely to identify people at risk for specific medical conditions. So, in our example, the sensitivity is 60% and the specificity is 82%. But as pointed out on our blog, the 92% figure represents the sensitivity of the test â not the accuracy â which is a very different concept. To understand all three, first we have to consider the situation of ⦠Because percentages are easy to understand we multiply sensitivity and specificity figures by 100. This test will correctly identify 60% of the people who have Disease D, but it will also fail to identify 40%. Sensitivity: A/(A + C) × 100 10/15 × 100 = 67%; The test has 53% specificity. Specificity: D/(D + B) × 100 45/85 × 100 = 53%; The sensivity and specificity are characteristics of this test. So 720 true negative results divided by 800, or all non-diseased individuals, times 100, gives us a specificity of 90%. Sensitivity and Specificity. We can then discuss sensitivity and specificity as percentages. However sometimes not all patients with that disease will have an abnormal test result (false negative) and sometimes a patient without the disease will have an abnormal test result (false positive). Assumption: You have a new rapid diagnostic test being evaluated for the screening of COVID-19, on the specific antibodies produces against the virus, SARS-CoV-2. Sensitivity and specificity are essential indicators of test accuracy and allow healthcare providers to determine the appropriateness of the diagnostic tool. The PPV and NPV are the other two basic measures of diagnostic accuracy. Example of Sensitivity and specificity. In other words, 45 persons out of 85 persons with negative results are truly negative and 40 individuals test positive for a disease which they do not have. Three very common measures are accuracy, sensitivity, and specificity. Accuracy is one of those rare terms in statistics that means just what we think it does, but sensitivity and specificity are a little more complicated. Diagnostic Specificity and diagnostic sensitivity Often a pathology test is used to diagnose a particular disease. The equation to calculate the sensitivity of a diagnostic test The specificity is calculated as the number of non-diseased correctly classified divided by all non-diseased individuals. Balanced Accuracy as described in [Urbanowicz2015]: the average of sensitivity and specificity is computed for each class and then averaged over total number of classes. References: Guyon2015 ( 1 , 2 ) Sensitivity vs specificity mnemonic. 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