I have never seen the issue of population normality addressed.

But we deal with the idea of statistically testing normality very often here.

And testing is about making statements about the population.

In practice it is impossible to ever know what the population distribution is so I am not sure what value it would be to know this.

What we really or ultimately are interested in, are the sampling distributions of the

test statistics, in order to perform the statistical tests. So, usually we not only are

uninterested in the data distribution within the sample, but we even are uninterested

in normality of the data distribution within the population. But AFAIK in case of a small

sample we need the assumption of normality in the population from which the sample

was drawn, in order to make correct statements about the sampling distribution of the

calculated test statistic. With larger samples, the central limit theorem applies.

Now, because the distribution within the sample is not of concern, or only to the

degree it can be used to infer statements about the distribution in the population,

my question was, how or why graphical methods, based just on sample data,

can be used to make such inferences.

With kind regards

K.