[LordDragonfang]

That's precisely what this is trying to model, yes. The standard computational way to simulate a binary event with probability p is to call rand() and check if rand(0,1) < p (or > 1-p, what I did). Or as you called it, an unfair coin flip.

This model is built on the assumptions that if candidates are actually totally equally likely to be picked (the null hypothesis for the experiment above), any given candidate has a p=.2 chance of being hired (given an arbitrary but reasonable hire vs interview ratio of 1:5). Which is just a weighted coin flip. This is indeed a binomial distribution, and my point is that results ±3% of the mean (p*M), even at M=1000, are still fairly probable. When comparing two such results, it's almost expected.

[timr]

The part where you did rand() < 0.8 ? 1 : 0 is fine. That's a Bernoulli trial with p=0.8

The part where you did this in a loop, with two calls per iteration, and then divided the counts and called it a percentage is wrong. It's certainly not a Binomial distribution. It's just the ratio of two binomial random variables.