Bayes' theorem describes the probability of an event based on prior knowledge of conditions that might be related to the event. It is fundamental in statistics, machine learning, and diagnostic testing.

P(A|B) = P(B|A) × P(A) / [P(B|A) × P(A) + P(B|¬A) × P(¬A)]

Where:
• P(A|B) is the posterior probability of A given B
• P(A) is the prior probability of A
• P(B|A) is the likelihood (true positive rate / sensitivity)
• P(B|¬A) is the false positive rate (1 — specificity)

The Sensitivity/Specificity mode models a clinical test scenario where prevalence, sensitivity, and specificity are given as percentages. The calculator automatically computes the false positive rate and applies Bayes' theorem.