Now remember what we are originally trying to find. We're trying to find where on the distribution of sample means a particular sample will lie. Not just for a simple population like this one, but for a huge population. And now we can do that. Because now we know that the distribution of means, where every mean is the mean of a sample of size n. This distribution has a standard deviation equal to the population standard deviation divided by the square root of n. This is called the central limit theorem. And it not only holds true for these simple populations but for any population. Because of the central limit theorem, we can have a population of any shape. And then let's say we draw a sample from it and calculate the mean, and then we draw another sample from it and calculate the mean. And we keep doing this, say a 100 times. Assuming the sample size is large enough, if we plot the distribution of means, we're going to get something that's relatively normal. With a standard deviation equal to the population standard deviation divided by the square root of the sample size. And we've been calling it SE so far. And that's because this is called the standard error. This is super cool, but I also understand that it's also super complicated. So we're going to go through a few more ways of looking at this, using applets and demonstrations. And then finally at the end of this lesson, go over an example where we would actually use this in real life.