Which metric is defined as TP divided by (TP plus FN)?

Which metric is defined as TP divided by (TP plus FN)?

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

Answer: (A)

Sensitivity is the proportion of people who truly have the condition and also test positive. It is calculated as TP divided by (TP plus FN). This tells you how good the test is at picking up the disease when it is actually present—the higher the sensitivity, the fewer true cases are missed (fewer false negatives). For example, if 20 people truly have the disease and the test correctly identifies 18 of them (TP = 18) but misses 2 (FN = 2), the sensitivity is 18 / (18 + 2) = 0.90 or 90%. This metric is different from specificity (which looks at those without the disease), Positive Predictive Value (which looks at how many positive results are true positives), and Negative Predictive Value (which looks at how many negative results are true negatives). The given formula matches sensitivity because it focuses on the ratio of true positives to all actual positives.

Sensitivity is the proportion of people who truly have the condition and also test positive. It is calculated as TP divided by (TP plus FN). This tells you how good the test is at picking up the disease when it is actually present—the higher the sensitivity, the fewer true cases are missed (fewer false negatives). For example, if 20 people truly have the disease and the test correctly identifies 18 of them (TP = 18) but misses 2 (FN = 2), the sensitivity is 18 / (18 + 2) = 0.90 or 90%.

This metric is different from specificity (which looks at those without the disease), Positive Predictive Value (which looks at how many positive results are true positives), and Negative Predictive Value (which looks at how many negative results are true negatives). The given formula matches sensitivity because it focuses on the ratio of true positives to all actual positives.