Confirmatory testing logic applies Bayesian reasoning to sequential diagnostic testing. The post-test probability after a screening test becomes the pre-test probability for the confirmatory test. Sequential testing with independent tests dramatically changes probability: a positive screening test (high sensitivity) followed by a positive confirmatory test (high specificity) yields a very high positive predictive value even in low-prevalence populations. This is why two-step algorithms are used: step 1 maximizes sensitivity to avoid missing disease; step 2 maximizes specificity to minimize false positives. Key concepts include the importance of disease prevalence on predictive values, the independence assumption for sequential tests, and knowing when additional testing will not meaningfully change clinical probability (testing threshold and treatment threshold).