On 10/22/2009 9:48 AM, rkevinbur...@charter.net wrote:
I am having a hard time interpreting the results of the 'shapiro.test' for
normality. If I do ?shapiro.test I see two examples using rnorm and runif. When
I run the test using rnorm I get a wide variation of results. Most of this may
be from variability of rnorm, samll sample size (limited to 5000 for the test),
etc but if I repeat the test multiple times I can get:
shapiro.test(rnorm(4900, mean = 5, sd = 3))
Shapiro-Wilk normality test
data: rnorm(4900, mean = 5, sd = 3)
W = 0.9994, p-value = 0.09123
With a p-value of 0.09 it doesn't give me alot of confidence that either rnorm is
producing a normal distirbution of this test is very reliable. Obivously this test has
gained wide acceptance so I was wondering if I am expecting too much? Is there a
"better" test?
I think you don't understand what p-values mean. If the null is true, p
is distributed as U(0,1).
You can see
Murdoch, D.J., Tsai, Y.-L. and Adcock, J. (2008). P-values are random
variables. {\em The American Statistician}, 242-245.
for more details (and exceptions to this very general rule).
Duncan Murdoch
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