The traditional assumption that the test data is drawn from the same distribution as the training data, makes it difficult to understand how a model will perform in settings where that assumption does not hold.
if the difficulty of test set data is known, we can better understand what kind of examples a given model performs well on, and specific instances where a model underperforms (e.g.