[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]
Re: Kolmogorov-Smirnov test
From: |
Hamish Allan |
Subject: |
Re: Kolmogorov-Smirnov test |
Date: |
Thu, 17 Nov 2005 19:36:45 +0000 |
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.
Thanks,
Hamish
-------------------------------------------------------------
Octave is freely available under the terms of the GNU GPL.
Octave's home on the web: http://www.octave.org
How to fund new projects: http://www.octave.org/funding.html
Subscription information: http://www.octave.org/archive.html
-------------------------------------------------------------