Based on the table showing test results, what is the negative predictive value?

Based on the table showing test results, what is the negative predictive value?

• A. 88.5%
• B. 92.5%
• C. 95%
• D. 97%

Answer: (B)

The thing being tested is negative predictive value: when a test result is negative, how likely is it that the person truly does not have the disease. It is calculated as true negatives divided by the sum of true negatives and false negatives (NPV = TN / (TN + FN)). In the table, identify the count of people without the disease who tested negative (true negatives) and the count of people with the disease who tested negative (false negatives), then compute the ratio TN ÷ (TN + FN). The resulting percentage tells you how trustworthy a negative result is in that dataset. If the table yields 92.5% for this ratio, that matches the correct choice. Remember that NPV depends on disease prevalence in the population being tested—the lower the prevalence, the higher the NPV tends to be. For example, if TN = 185 and FN = 15, NPV = 185 / (185 + 15) = 0.925, i.e., 92.5%.

The thing being tested is negative predictive value: when a test result is negative, how likely is it that the person truly does not have the disease. It is calculated as true negatives divided by the sum of true negatives and false negatives (NPV = TN / (TN + FN)). In the table, identify the count of people without the disease who tested negative (true negatives) and the count of people with the disease who tested negative (false negatives), then compute the ratio TN ÷ (TN + FN). The resulting percentage tells you how trustworthy a negative result is in that dataset. If the table yields 92.5% for this ratio, that matches the correct choice. Remember that NPV depends on disease prevalence in the population being tested—the lower the prevalence, the higher the NPV tends to be. For example, if TN = 185 and FN = 15, NPV = 185 / (185 + 15) = 0.925, i.e., 92.5%.