This is not a uniform distribution: const randIntRange = (i, j, engine = defaultEngine) => { const min = Math.ceil(i) const max = Math.floor(j)

This is not a uniform distribution: const randIntRange = (i, j, engine = defaultEngine) => { const min = Math.ceil(i) const max = Math.floor(j) return Math.floor(engine() * (max - min + 1)) + min } If you assume engine() outputs, say, 64 bits of entropy, any number range that is not an even factor of 2^64 will exhibit intermittent bias where the numbers are rounded. The only correct way to do this is to do rejection sampling: generate an integer x with log2(max - min) bits and if x >= max - min retry until you do: function randBelow(n) { const nbits = Math.ceil(Math.log2(n)); while (true) { const x = getRandBits(nbits); if (x < n) { return x; } } } function randIntHalfOpenRange(min, max) { return min + randBelow(max - min); } function randIntRange(min, max) { return randIntHalfOpenRange(min, max + 1); } The primitive you want for building distributions on is not to generate a floating point number between 0.0 and 1.0, but to generate an integer of at most x bits.