Which Leakage Types Matter?

ArXi:2604.04199v1 Announce Type: new 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.