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Normality test

What is the distribution of the p-value when the null hypothesis is true?

This is an important question that unfortunately tends to get glossed over or left out completely in many courses due to the amount of information that needs to be packed into them.

For most appropriate tests, when the null hypothesis is true and all other assumptions are true, the p-value is distributed as uniform(0,1).  Hence the probability of a type I error is alpha for any value of alpha.  Therefore, when the null is true, the likelihoods of getting a p-value of 0.99, 0.051, 0.049, or 0.0001 are all exactly the same.

If you want a high p-value for a normality test, just collect only 1 data point, no matter what it's value is, it is completely consistant with the assumption that it came from some normal distribution (p-value=1).

For large sample sizes the important question is not "did this data come from an exact normal distribution?", but rather, "Is the distribution this data came from close enough to normal?".

If you really feel the need for a test of normality in large sample sizes, then see this post:
http://finzi.psych.upenn.edu/R/Rhelp02a/archive/136160.html

Hope this helps,

--
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org
(801) 408-8111
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