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Re: Kolmogorov-Smirnov test
From: |
Mike Miller |
Subject: |
Re: Kolmogorov-Smirnov test |
Date: |
Thu, 17 Nov 2005 14:20:31 -0600 (CST) |
On Thu, 17 Nov 2005, Hamish Allan wrote:
On 17 Nov 2005, at 19:25, Mike Miller wrote:
In statistical testing, a valid p-value has a uniform distribution when
the null hypothesis is true. In the K-S two-sample test, the null
hypothesis is that the two distributions are the same. We reject the
null when p is small. The probability of p < .05, for example, equals
.05 when the null is true, and this is because p is uniform when the
null is true.
Right, I certainly didn't understand this, and now the results seem less
strange :)
So is there any way of doing what I originally wanted to do: determine
how likely two given datasets are to have been drawn from the same
distribution?
In fact, what I want to do is to try to determine how "representative" a
subsample is of the distribution of the original sample. This involves
plenty of ties... so should I be going about this a whole different way?
Any pointers gratefully accepted.
The k-s test (not k-s2) is used for testing if a sample came from a given
population. In your case the "given population" is given by the original
sample. It tests a particular kind of deviation though, maybe not what
you want to know. I don't know what it does with ties in the Octave
version.
Mike
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