Sampling bias during data collection can result in severe covariate shift, leading to models that fail in practice. For example, a medical algorithm designed to detect a disease using blood samples might be trained on a dataset consisting of sick older patients alongside healthy college students. Because the cohorts differ drastically in unrelated factors like age and hormone levels, the classifier might achieve high accuracy by learning these spurious features instead of genuine disease indicators. When deployed on real patients, the test will likely fail due to the extreme covariate shift between the training sample and the actual patient population.