It’s the same distribution as a coin flip in the limiting case.
But in the particular case of the OP where it’s exactly 50%, the chances are not good to hit 50% exactly with coin flips.
I think I gave the example of 10 flips above. With 10 flips, hitting exactly 5 only takes place 24.6% of the time. But hitting between 4 and 6 happens over 67% of the time.
With 1000 flips, hitting exactly 500 is very rare, but hitting between 400 and 600 is basically guaranteed.
So, yes, variance matters. This is one of those weird bits about the Law of Large Numbers. If the selection was based on coin flips instead of an exact 50% selection, the result would not be EXACTLY 50% but it would be within a “small” (for given definition of ‘small’) neighborhood of exactly 50% with a very high probability.
When you flip the coin 1 trillion times, getting 1 million more heads than tails is basically a rounding error. So the number of times the result varies from the perfect 50% behavior can get big in absolute terms. But the percentage it varies from the perfect 50% behavior gets arbitrarily close to 0 at the same time.
Arbitrarily dye some of the coins blue. Label the blue coins “humans”. That’s the proof, basically.
After that, re-label the blue coins “non-humans”. Unless you have a very small number of blue coins (like less than 100), they’ll get selected with the same 50% behavior.
The trick is the independence of selection. We are assuming (supported by the OP) that “life” is chosen 1 at a time until half are reached. And we are assuming things are uniformly mixed so the probability ‘blue’ life is chosen at the same proportion it represents in the bucket.
As before, things only break down when that assumption is violated - the number of samples gets so small that the same proportionality no longer holds.