If, by quality, you mean "indistinguishable from true randomness" then there are many valid metrics. One important metric that many non-CS PRNGs have, but many

If, by quality, you mean "indistinguishable from true randomness" then there are many valid metrics. One important metric that many non-CS PRNGs have, but many CSPRNGs do not have, is a provable deterministic cycle length for all seeds. These characteristics don't just make Monte Carlo simulations converge quicker, they also do things like guarantee that your 52 deck shuffle will definitely produce the one shuffle that gives player two a royal flush on the flop in a round of texas hold 'em. Or, more generally, they guarantee that your Array.shuffle() method is unbiased for a reasonable sized array. In general you need true randomness and/or a long cycle linear-transformation PRNG to make those sorts of guarantees. It's also nice that they're fast. That said, I do also agree that the scenarios in which these things matter are rather rare, and most people should probably just use a CSPRNG by default. Do you really think a CSPRNG is fast enough for your standard library's array shuffle though? Wouldn't you rather have something like xorshift1024* that's probably > an order of magnitude faster? Maybe a CSPRNG is fast enough... I don't have all the numbers. So this is an honest question.