Abstract
Twenty-eight within-subject counterfactual experiments across 2,047 tabular datasets, plus a boundary experiment on 129 temporal datasets, measuring the severity of four data leakage classes in machine learning. Class I (estimation - fitting scalers on full data) is negligible: all nine conditions produce |\Delta\text{AUC}| \leq 0.005. Class II (selection - peeking, seed cherry-picking) is substantial: ~90% of the measured effect is noise exploitation that inflates reported scores. Class III (memorization) scales with model capacity: d_z = 0.37 (Naive Bayes) to 1.11 (Decision Tree). Class IV (boundary) is invisible under random CV. The textbook emphasis is inverted: normalization leakage matters least; selection leakage at practical dataset sizes matters most.
