Which value represents the proportion of actual positives correctly identified by the test?

The true positive rate, also known as sensitivity, measures the proportion of actual positives that are correctly identified by a test. In simpler terms, it quantifies how effectively a test identifies cases that truly have the condition in question. For example, if a test has a true positive rate of 90%, it means that 90% of individuals who truly have the condition are correctly identified as positive by the test.

This concept is crucial in various fields, especially in diagnostics, where the ability to correctly identify positive cases can significantly impact treatment and outcomes. A high true positive rate indicates that the test is reliable in detecting those who have the condition, thus minimizing the chances of missing actual cases (known as false negatives).

In contrast, specificity refers to the proportion of actual negatives correctly identified, the false negative rate represents the proportion of actual positives incorrectly identified as negatives, and negative predictive value indicates the likelihood that a negative test result reflects the absence of the condition. Understanding these different metrics is vital when evaluating the overall performance of diagnostic tests in a clinical setting.