Which metric is defined as TN divided by (TN plus FP)?

Which metric is defined as TN divided by (TN plus FP)?

• A. Specificity
• B. Sensitivity
• C. Positive Predictive Value
• D. Negative Predictive Value

Answer: (A)

Specificity measures how well a test correctly identifies those without the condition. It is calculated as the number of true negatives divided by the sum of true negatives and false positives (Specificity = TN / (TN + FP)). This means that if non-diseased individuals are frequent in the tested group, a test with high specificity will produce few false positives, accurately ruling out disease in people who don’t have it. To see it in context, imagine a group where many people do not have the condition. If 80 people are disease-free and 5 of them test positive (false positives), then 75 are true negatives. Specificity would be 75 divided by 80, about 93.75%. The more false positives you have, the lower the specificity becomes. Other metrics operate with different true/false counts. Sensitivity looks at how many actual positives you correctly identify (TP/(TP+FN)), reflecting how good the test is at detecting disease when it’s present. Positive Predictive Value (TP/(TP+FP)) tells you the probability that a positive test truly indicates disease, and Negative Predictive Value (TN/(TN+FN)) tells you the probability that a negative test truly indicates no disease. PPV and NPV depend on how common the disease is in the population, whereas specificity is focused on correctly identifying those without disease.

Specificity measures how well a test correctly identifies those without the condition. It is calculated as the number of true negatives divided by the sum of true negatives and false positives (Specificity = TN / (TN + FP)). This means that if non-diseased individuals are frequent in the tested group, a test with high specificity will produce few false positives, accurately ruling out disease in people who don’t have it.

To see it in context, imagine a group where many people do not have the condition. If 80 people are disease-free and 5 of them test positive (false positives), then 75 are true negatives. Specificity would be 75 divided by 80, about 93.75%. The more false positives you have, the lower the specificity becomes.

Other metrics operate with different true/false counts. Sensitivity looks at how many actual positives you correctly identify (TP/(TP+FN)), reflecting how good the test is at detecting disease when it’s present. Positive Predictive Value (TP/(TP+FP)) tells you the probability that a positive test truly indicates disease, and Negative Predictive Value (TN/(TN+FN)) tells you the probability that a negative test truly indicates no disease. PPV and NPV depend on how common the disease is in the population, whereas specificity is focused on correctly identifying those without disease.