Before revision:  In some cases, even worst-case splits  under-estimate the error observed on new samples of in-domain data, i.e., the data that models should minimally generalize to at test time.
After revision:  We can also split data in biased or adversarial ways, e.g., training on short sentences and evaluating on long ones. Biased sampling has been used in domain adaptation to simulate real-world drift; this is known as the covariate shift assumption. In NLP, however, even worst-case splits under-estimate the error observed on new samples of in-domain data, i.e., the data that models should minimally generalize to at test time.