  There will be philosophically significant consequences if the Commonplace Thesis is incorrect, and if ordinary usage is misleading. For example, it is intuitively plausible that if an event is truly random it cannot be explained (if it happens for a reason, it isn’t truly random). It might seem then that the possibility of probabilistic explanation is undermined when the probabilities involved are genuine chances. Yet this pessimistic conclusion only follows under the assumption, derived from the Commonplace Thesis, that all chancy outcomes are random. Another interesting case is the role of random sampling in statistical inference. If randomness requires chance, then no statistical inferences on the basis of ‘randomly’ sampling a large population will be valid unless the experimental design involves genuine chance in the selection of subjects. But the rationale for random sampling may not require chance sampling—as long as our sample is representative, those statistical inferences may be reliable. But in that case, we’d be in a curious situation where random sampling wouldn’t have much to do with randomness, and whatever justification for beliefs based on random sampling that randomness is currently thought to provide would need to be replaced by something else.