Sensitivity and specificity are intrinsic properties of diagnostic tests. Sensitivity (true positive rate) = TP/(TP+FN) — 'of people who HAVE the disease, what proportion does the test detect?' High sensitivity means few false negatives; a NEGATIVE result reliably rules OUT (SnNOut). Specificity (true negative rate) = TN/(TN+FP) — 'of people WITHOUT disease, what proportion tests negative?' High specificity means few false positives; a POSITIVE result reliably rules IN (SpPIn). Predictive values depend on prevalence: PPV = TP/(TP+FP); NPV = TN/(TN+FN). As prevalence decreases, PPV decreases even if sensitivity/specificity remain constant. This is why screening in low-prevalence populations generates many false positives. Likelihood ratios combine sensitivity and specificity: positive LR = sensitivity/(1-specificity); negative LR = (1-sensitivity)/specificity. LR+ >10 or LR- <0.1 provide strong evidence.