Every machine learning engineer learns early that leakage inflates validation scores. We check for target leakage. We check for train-test contamination. We use time-based splits instead of random ones on temporal data. Then we build a feature over a rolling window, evaluate the model, and quietly reintroduce a related problem that no leakage check is designed to catch. It is not leakage. The trouble is a sample size you never actually had. Where it comes from Temporal mode...