I think the floating-point precision interpretation is negated by the author's suggestion that the loss of precision is "because of the small sample size". I

I think the floating-point precision interpretation is negated by the author's suggestion that the loss of precision is "because of the small sample size". I can sort-of see how a purely analytical solution might be susceptible to rounding issues (and I say sort-of because I don't think such an approach would actually include multiplying millions of fractions, and the straightforward approach involves numbers near 1 instead of near 0), but the reference to "sample size" makes me think that this was a numerical simulation. But presuming the author ran the simulation more than once (wouldn't you do this if you had such a surprising result?) I don't see how there could be any consistent effect unless there was a bug in the logic of the program. Unless maybe they ran it multiple times with the same random seed and got exactly the same results?