00:01
The central limit theorem states that if we take a sample size sufficiently large, our distribution will be normally distributed.
00:11
What it means is so eventually if we increase and increase and increase and increase our sample size, our distribution is going to at some point reach this condition, being a normal distribution, having this bell shape in which, well, we have a mean, and the mean is going to be a place.
00:31
Approximately at the middle, sometimes it's a little bit to the right or a little bit to the left, but it's going to be to the middle or at the center as it says the central limit theorem and then we're going to find the standard deviations to the right or standard deviations to the left to see how well distributed or how spread our data is, but this is the main idea.
00:54
Eventually as we get more and more and more and more and more data, every population is going to look like this bell shape, like a normal distribution.
01:04
So a big sample size, we're talking about, well, there are actually two type of samples.
01:09
We have the c scored, which is for sample size greater than 30, and we have the t scored, which is for sample size, smaller or equal than 30...