James,

I assume that you use package diptest.
Look up
?qDiptab
for a table of quantiles from which you can obtain your p-value.

data(qDiptab)
qDiptab

You then look up your statistic value (0.074) for a suitable n
(n=30, say), and you take one minus the Pr value you find on top of the table.
Using n=30 gives you Pr between 0.8 and 0.9, so 0.2>p>0.1.
However, the next largest value of n (50) would lead to 0.05>p>0.02 leading to possibly different conclusions. Unfortunately this means that you are in a kind of ambiguous borderline situation for the table, though 33 is so much closer to 30 than to 50 that it seems that your result is at least not significant at 0.05 level.

By the way, you can simulate a p-value yourself by repeating dip(runif(33)) lots of times.

Hope this helps,
Christian

On Mon, 6 Jul 2009, James Allsopp wrote:

Hi,
I just got a value for the dip test out of my data of 0.074 for a sample
size of 33. I'm trying to work out what this actually means though?
Could someone help me relate this to a p-value?

Thanks
James

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*** --- ***
Christian Hennig
University College London, Department of Statistical Science
Gower St., London WC1E 6BT, phone +44 207 679 1698
chr...@stats.ucl.ac.uk, www.homepages.ucl.ac.uk/~ucakche

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