High-accuracy classifier can still miss every useful research candidate

A synthetic teaching example shows that a screening classifier scoring 95% accuracy on 100 records can find none of the five positive candidates. The article explains why accuracy alone hides which cases were missed. Precision and recall are compared across two constructed prediction sets, showing that catching more positives can come with more false positives. The author stresses that the labels and predictions are invented for illustration and are not measurements from a real model or laboratory. A short Python audit script is provided that computes confusion-matrix counts, accuracy, precision and recall without external data or dependencies.