Sampling Distribution in Statistics

Description

Using different equations we can find the "meanest of the mean" in our sampling distribution.

Transcript
Sampling Distribution in Statistics Say you have a population that has all the usual stuff like a mean of µ and a standard deviation of ?, then take a bunch of samples from that population and then find the means of those samples. The central limit theorem states that no matter how whacked out and non-normal those samples look, if each sample sizes over 30, when you put all those means in a distribution the sampling distribution, the sampling distribution you get will be normal, as in bell shaped. The mean of this normal sampling distribution, which we’ll call the meanest of the means, will be just about equal to the mean of the original population. And the standard deviation of our sample mean will be equal to the standard deviation of the population divided by the square root of the sample size we took. Just as we can use the mean of the sampling distribution to test the reliability of our sample mean, we can use the standard deviation of the sampling distribution to gauge the reliability of the standard deviation for our sample.
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