The mean of our sampling distribution of our sample proportion is just going to be equal to the mean of our random variable X divided by N. It's just going to be the mean of X divided by N, which is equal to what? Well, the mean of X is N times P. This is N times P. You divided it by N, you're going to get P. And that makes sense. One way to think about it, the expected value for your sample proportion is going to be the proportion of gumballs that you actually see. And so this is also a good indicator that this is going to be a reasonably unbiased estimator. Now let's think about the standard deviation for our sample proportion. Well, we can just view that as our standard deviation of our binomial random variable X divided by N. So this is going to be equal to the square root of N times P times one minus P, all of that over N, which is the same thing as if we put this, if we divide by N inside the radical, it'd be the same thing as the square root of N, P times one minus P over N squared. Divide the numerator and denominator by N, you will get the square root of P times one minus P, all of that over N. And so in this particular situation where our parameter is 0.6, where our population parameter is 0.6, so it's going to be 0.6, that's the true proportion for our population.