## ← The Central Limit Theorem - Intro to Descriptive Statistics

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Showing Revision 4 created 05/24/2016 by Udacity Robot.

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