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Can you explain why a test with 95% sensitivity might identify only 1% of affected people in the general population? The visual approach in this article should make the reason clearer I first encountered sensitivity and specificity in medical school. That is, I remember my eyes glazing over on being told that “sensitivity = TP/TP+FN, where TP is the number of true positives and FN is the number of false negatives.” As a doctor I continued to encounter sensitivity and specificity, and my bewilderment turned to frustration–these seemed such basic concepts; why were they so hard to grasp? Perhaps the left (logical) side of my brain was not up to the task of comprehending these ideas and needed some help from the right (visual) side. What follows are diagrams that were useful to me in attempting to better visualise sensitivity, specificity, and their cousins positive predictive value and negative predictive value.I will be using four symbols in these diagrams (fig 1). Let us start by looking at a hypothetical population (fig 2). The size of the population is 100 and the number of people with the disease is 30. The prevalence of the disease is therefore 30/100 = 30%.Fig 1 Key to symbolsFig 2 Hypothetical populationNow let us imagine applying a diagnostic test for the disease to this population and obtaining the results shown in figure 3. The test has correctly identified most, but not all of the people with the disease. It has also correctly labelled as disease …

Understanding sensitivity and specificity with the right side of the brain