Sensitivity and specificity definitions.

Sensitivity and specificity definitions.

• A. Sensitivity is ability to correctly identify true positives; specificity is ability to correctly identify true negatives.
• B. Sensitivity is ability to correctly identify true negatives; specificity is ability to correctly identify true positives.
• C. Sensitivity is probability of observing data under null; specificity is probability of rejecting null.
• D. Sensitivity is how quickly a test yields results; specificity is how much it costs.

Answer: (A)

Sensitivity and specificity measure how well a test classifies people by their true disease status. Sensitivity is the ability to correctly identify those who truly have the condition (true positives). Specificity is the ability to correctly identify those who truly do not have the condition (true negatives). In formulas, sensitivity = true positives divided by all who actually have the disease, and specificity = true negatives divided by all who actually do not have the disease. This makes sense in practice: a test with high sensitivity is good at catching cases and minimizes missed diagnoses (false negatives), while a test with high specificity is good at ruling out people who don’t have the disease and minimizes false alarms (false positives). The option that states this relationship accurately is the right one. The other statements either swap the roles of true positives and true negatives, refer to probabilities under a null hypothesis, or describe unrelated aspects like speed or cost.

Sensitivity and specificity measure how well a test classifies people by their true disease status. Sensitivity is the ability to correctly identify those who truly have the condition (true positives). Specificity is the ability to correctly identify those who truly do not have the condition (true negatives). In formulas, sensitivity = true positives divided by all who actually have the disease, and specificity = true negatives divided by all who actually do not have the disease.

This makes sense in practice: a test with high sensitivity is good at catching cases and minimizes missed diagnoses (false negatives), while a test with high specificity is good at ruling out people who don’t have the disease and minimizes false alarms (false positives). The option that states this relationship accurately is the right one. The other statements either swap the roles of true positives and true negatives, refer to probabilities under a null hypothesis, or describe unrelated aspects like speed or cost.