I have been reading this article on the random sampling distribution (RSD) and non-normal distributions. Basically, if I understand it correctly, the article proposes that the RSD of the mean of a sample of different size can be compared against the normal distribution using a comparison of kurtosis and skewness.
The logic of the author is that
as the sample size increases, the distribution of the means gets more and more normal. So with non-normal distributions, the sample size needed to detect the change in the average, which we are looking for, also has to be large enough so that the RSD is reasonably approximated by the normal distribution.
In figure 3 row 1, for example, I read that taking the RSD of the mean of 15,000 samples of 5 has a skewness of 0.207, kurtosis of 0.107 etc.
What I don't understand is how the number of samples the author draws in Figure 3 and Figure 4 under column 'n' (15,000 and 1,000) is chosen. It seems that they are chosen for convenience (i.e. 15,000 doesn't work so the author uses 1,000) instead.
I must be missing something. Can anybody explain how to choose
n (i.e., the second column in the two tables, not the
Variable column / row names)?