Not asking me, but yes. I think the underlying issue is whether you assume the process that generated the data yesterday is similar enough to the process that g

Not asking me, but yes. I think the underlying issue is whether you assume the process that generated the data yesterday is similar enough to the process that generated data today. In truth, all data points are unique and all data is high dimensional data. It only becomes workable in low dimensions once we make assumptions like "the process that produced this point yesterday is the same as the process that produced this second days point today." Separating the tests makes it slightly less difficult to test for violations of these assumptions (e.g., model drift). But otherwise it seems silly and arbitrary to commit yourself you two medium tests instead of one big test. At least two medium tests let you filter out likely failures if you allow yourself to quit midway. (This whole business of tests and halting is silly anyhow.)