How to test a large sample if it is normaly distributed?

Question

I am dealing with normaly testing of large samples. As stated here:

Is normality testing 'essentially useless'?

Normally testing is essentially useless, if the sample is too large. Even visual testing cannot give a clear statement about the distribution?

So, how to test a large sample if the distribution is normal distributed?

My purpose is, to test if the distribution is normaly distributed or not? — Le Max, Mar 17 '13 at 12:01
Why do you want to know? What will you do if it is normal? What will you do if it is not normal? Is this a testing of residuals from a regression? — Peter Flom, Mar 17 '13 at 12:23
Yes it is testing of residuals from a regression. However, I am interested how huge data sets are testen for normal distribution, and if its possible to get a meaningful result? — Le Max, Mar 17 '13 at 12:59

score 1 · Accepted Answer · answered Mar 17 '13 at 13:40

1

Testing residuals is the classic case where formal normality testing goes astray, see the first answer in the question you linked to. This isn't unique to normality testing, it's a problem with p-values for any very large data set - that is, results can be statistically highly significant but of no practical import.

But I think this may be an exact duplicate question, unless you can give some reason why it is different than the question you linked to.

answered Mar 17 '13 at 13:40

Peter Flom

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Ok so generally said, it depends on your goal how valid your answer about the normal distribution is? – Le Max Mar 17 '13 at 14:09
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It depends on the consequences of the amount of non-normality for the technique that you wish to use. – Peter Flom Mar 17 '13 at 14:12

How to test a large sample if it is normaly distributed?

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