So the central limit theorem, although, I you do a bunch of trials that look like a normal distribution, it definitely doesn't work for n equals 1. As n gets larger, though, it starts to make sense. Let's say if we got n equals 2, and I'm all just doing this in my head, I don't know what the actual distributions would look like, but then it still would be difficult for it to become an exact normal distribution, but then you can get more instances, you could get more, you know, you might get things from all of the above, but you can only get 2 in each of your baskets that you're averaging, you're only going to get 2 numbers, right? So you're never going to, let's see, I mean, for example, you're never going to get a 7.5 in your sampling distribution of the sample mean for n is equal to 2, because it's impossible to get a 7, and it's impossible to get an 8. So you're never going to get 7.5 as, so maybe when you plot, when you plot it, maybe, you know, maybe it looks like this, but there'll be a gap at 7.5, because that's impossible, and, you know, maybe it looks something like that. So it still won't be a normal distribution when n is equal to 2. So there's a couple of interesting things here.