Well, it's definitely not going to look like a normal distribution. It's going to look as if you're going to have a couple of ones, you're going to have a couple of twos, you're going to have more threes like that, you're going to have no fours, you're going to have a bunch of fives, you're going to have some sixes that look like that, and then you're going to have a bunch of nines. So there your sampling distribution of the sample mean for an n of 1 is going to look, I don't care how many trials you do, it's not going to look like a normal distribution. 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.