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🧪 Bayesian Test Accuracy

Interactive Bayes' theorem simulator: sweep disease prevalence, test sensitivity and specificity and watch a 10,000-person natural-frequency grid and the real PPV/NPV recompute live, showing why a positive result on a rare disease is so often a false alarm.

Medical Technology & Imaging Physics2DModerate60 FPS📱 Mobile-adapted

This simulator computes real Bayes'-theorem statistics for a diagnostic screening test and renders them as a 10,000-person natural-frequency grid, so the counts are visible rather than abstract. Set a disease prevalence, a test sensitivity and a test specificity, and every one of the 10,000 squares is recoloured according to its actual simulated disease status and test result — true positive, false positive, false negative or true negative — with the exact counts derived from the same PPV/NPV formulas printed in the panel. It is the standard illustration of why, for a rare disease, most positive results on an accurate test are still false alarms: shrink prevalence and watch the false-positive block outgrow the true-positive block even as sensitivity and specificity stay high.

⚙ Under the hood

Explore how disease prevalence affects test outcomes in a grid of 10,000 simulated people. Most positive results are false positives for rare diseases.

bayes theoremdiagnostic testpositive predictive valuefalse positivebase rate fallacysensitivityspecificityscreening

2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install