Final answer:
Sensitivity is the likelihood of a test to detect an infection if present, making false negatives unlikely. Specificity measures a test's ability to return a negative result when no infection is present, minimizing false positives. Positive predictive value indicates the probability that a positive result is true, and negative predictive value shows the likelihood that a negative result is accurate.
Step-by-step explanation:
The terms sensitivity, specificity, positive predictive value, and negative predictive value are key concepts in evaluating the accuracy of diagnostic tests.Sensitivity is the probability that a test will correctly identify patients with the disease, reflecting the test's ability to give a positive result for those who are infected. High sensitivity means there is a low chance of a false negative, where the test fails to detect an existing infection.
Specificity is the probability that a test will correctly identify patients without the disease, indicating the test's ability to return a negative result for those who are not infected. High specificity implies a low chance of a false positive, where the test incorrectly indicates an infection in a healthy person.The positive predictive value (PPV) is the likelihood that a person with a positive test result actually has the disease, while the negative predictive value (NPV) is the likelihood that a person with a negative test result is truly disease-free.