Comment by syntacticsalt

A Uniform(0, 1) PRNG is the right primitive on which to base a PRNG library for arbitrary real-valued random variables because any real-valued random variable can be represented as an inverse quantile transform of a Uniform(0, 1) random variable. While I agree that only providing this primitive risks footguns as described in the article, I'm skeptical that providing a better UI alone would meaningfully reduce the risk of such footguns because availability and convenience is no guarantee of use if a prevailing attitude of users is that they know better and don't need it. Bisection search is simpler than rejection sampling or inverse transform sampling, yet it's common to see buggy, hand-rolled implementations of bisection search despite wide availability of library implementations with better UI ergonomics than PRNGs. I think wider use of fuzz testing or deterministic simulation testing really is necessary to disabuse people of that notion, along with more articles like the above explaining why hand-rolling an adapter to a uniform PRNG is a false economy compared to proven implementations with vetted statistical properties.