Twenty-eight within-subject counterfactual experiments across 2,047 iid tabular datasets, plus a boundary experiment on 129 temporal datasets, measure the severity of four data leakage classes in machine learning. Class I (estimation: fitting scalers on full data) is negligible: all nine conditions produce $|ΔAUC| \leq 0.005$. Class II (selection: peeking, seed cherry-picking) is substantial: the measured effect is consistent with about 90% noise exploitation inflating reported scores. Class III (memorization) scales with model capacity: $d_z$ = 0.37 (Naive Bayes) to 1.11 (Decision Tree) at 10% duplication. Class IV (boundary) is invisible under random cross-validation. Within this iid tabular regime, the textbook emphasis is inverted: normalization leakage matters least; selection leakage at practical dataset sizes matters most.

39 pages, 6 figures, 13 tables. Companion to arXiv:2603.10742