Biostatistics Essentials

Sensitivity, specificity, positive predictive value, and negative predictive value are core measures used to judge how well a medical test performs. They help students and clinicians decide whether a test is good for screening, confirming disease, or reassuring a patient after a negative result. These ideas connect statistics directly to patient care, because the same test can be useful in one setting and misleading in another. Understanding them prevents common errors in interpreting lab tests, imaging, and bedside screening tools.

Sensitivity and specificity describe the test itself by comparing results to the true disease status, while PPV and NPV describe what a positive or negative result means for the patient in a specific population. The standard framework is the 2 x 2 table with true positives, false positives, true negatives, and false negatives. Disease prevalence strongly affects PPV and NPV, even when sensitivity and specificity stay the same. In practice, highly sensitive tests are often used to rule out disease, and highly specific tests are often used to rule in disease.