Question

anyone know this question:

What conditions (and theorem) must be met in order for a sampling distribution of means to be approximated by a normal distribution?

Answer 1

@jim_thompson5910 plz help me bro

Answer 2

This page may help

http://stattrek.com/sampling/sampling-distribution.aspx

Answer 3

yes i found this page aswell, but im having trouble rephrasing it, i do not wont to copy the site

Answer 4

so tell me if this works: let me think for a seoncd

Answer 5

From that page,

http://prntscr.com/b5rbp9

Answer 6

this is my answer for the theorem part ok:

basically it means that the sampling distribution of the mean of a random independetn variable should be normal, and if not normal then close to it

Answer 7

Let X be any distribution of random values. In other words, X represents all of the values of people in the population


If X is a normal distribution, then the xbar distribution is also normal. So the distribution of sample means will be normal. This is for any sample size n.


If X is non-normal, then the xbar distribution will only be normal if and only if n > 30 (some books/teachers are more strict and go for n > 40; if things are really skewed, then use a much larger n). This is what the central limit theorem (CLT) is saying


If n is too small, then you may have to use the student T distribution