Which metric is calculated from the 2x2 contingency table to measure the ability to identify non-cases (true negatives)?

Which metric is calculated from the 2x2 contingency table to measure the ability to identify non-cases (true negatives)?

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

Answer: (C)

Specificity is the measure that reflects how well a test identifies people who do not have the condition. From the 2x2 table, it is calculated as true negatives divided by all people without the disease (TN + FP). In other words, it answers: among those who are disease-free, how often does the test correctly yield a negative result? This is why specificity is the best answer for identifying non-cases. Think about the other metrics: sensitivity looks at the ability to identify true cases (disease positives) rather than non-cases, so it’s not about non-cases. Positive predictive value focuses on the proportion of positive test results that are true positives, which depends on how common the disease is and doesn’t measure non-cases directly. Accuracy captures overall correctness (both true positives and true negatives) but doesn’t isolate performance on non-cases specifically. Example: if 80 people are disease-free and the test correctly marks 70 as negative (true negatives) and wrongly marks 10 as positive (false positives), the specificity is 70/(70+10) = 0.875, showing the test’s ability to identify non-cases among the disease-free group.

Specificity is the measure that reflects how well a test identifies people who do not have the condition. From the 2x2 table, it is calculated as true negatives divided by all people without the disease (TN + FP). In other words, it answers: among those who are disease-free, how often does the test correctly yield a negative result? This is why specificity is the best answer for identifying non-cases.

Think about the other metrics: sensitivity looks at the ability to identify true cases (disease positives) rather than non-cases, so it’s not about non-cases. Positive predictive value focuses on the proportion of positive test results that are true positives, which depends on how common the disease is and doesn’t measure non-cases directly. Accuracy captures overall correctness (both true positives and true negatives) but doesn’t isolate performance on non-cases specifically.

Example: if 80 people are disease-free and the test correctly marks 70 as negative (true negatives) and wrongly marks 10 as positive (false positives), the specificity is 70/(70+10) = 0.875, showing the test’s ability to identify non-cases among the disease-free group.