On 11/17/2009 10:07 AM, rkevinbur...@charter.net wrote:
This is probably an even more basic question but shapiro.test return both the
statistic (w) and the significance (pw) of the statistic. For this test the
null-hypothesis is that the distirbution is not normal so very small values of
pw would mean that there is very little chance that the distiribution is not
normal. Correct?
I think you have an extra "not" in your description of the null. But
more importantly, a small p-value just means that you had a value of the
test statistic that would be unusually extreme if the null was true.
Whether that means there is a small probability of the null being true
depends on information outside the test. It's essentially a Bayesian
question, and Bayesians wouldn't base their decision on just the p-value.
For example, if I flip a fair coin 100 times and see 30 heads,
binom.test() tells me that the p value would be 7.85e-05. That doesn't
mean the probability that the coin is fair is low: it just means I saw
an unusually small number of heads.
Now if I wasn't sure whether the coin was fair or not, I'd take the low
p-value as evidence that it was not. But the p-value alone isn't
sufficient to let me calculate a probability of the null being true.
Duncan Murdoch
Thank you.
Kevin
---- Johannes Graumann <johannes_graum...@web.de> wrote:
Markus Mehrwald wrote:
> Hi all,
>
> I am completely new to R and my knowledge of statistics is quite small
> so I hope you can help my.
> I have three dimensional point data which represents (and this is what I
> do not know for sure) a normal distribution. Now I want to test if this
> is true or not and as I can remember from statistics lessons I can use
> Chi-Square test for distribution test. BUT: I have realy no idea how to
> do this with R and additionally if my assumptions are correct and if
> this is possible with R at all.
>
> Thank you very much in advance for any answer.
> Markus
See
?shapiro.test
or
?ks.test
HTH, Joh
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