Thanks,

would it be possible to give an example of how I
can have more specific null hypothesis in R?
I am not aware of how to specify it for the K-S test in R.

And repeating my second question, what is a good way to measure the
difference between
observed and expected samples? Is the D statistic of the KS test a good
choice?

Nitin

On Thu, Aug 21, 2008 at 7:40 PM, Moshe Olshansky <m_olshansky@yahoo.com>wrote:

> Hi Nitin,
>
> I believe that you can not have null hypothesis to be that A and B come
> from different distributions.
> Asymptotically (as both sample sizes go to infinity) KS test has power 1,
> i.e. it will reject H0:A=B for any case where A and B have different
> distributions.
> To work with a finite sample you must be more specific, i.e. your null
> hypothesis must be not that A and B just have different distributions but
> must be more specific, i.e that their means are different by at least
> something or that certain distance between their distributions is bigger
> than something, etc. and such hypotheses can be tested (and rejected).
>
>
> --- On Fri, 22/8/08, Nitin Agrawal <NITINA.A+rhelp@gmail.com<NITINA.A%2Brhelp@gmail.com>>
> wrote:
>
> > From: Nitin Agrawal <NITINA.A+rhelp@gmail.com<NITINA.A%2Brhelp@gmail.com>
> >
> > Subject: [R] Null and Alternate hypothesis for Significance test
> > To: r-help@r-project.org
> > Received: Friday, 22 August, 2008, 6:58 AM
> > Hi,
> > I had a question about specifying the Null hypothesis in a
> > significance
> > test.
> > Advance apologies if this has already been asked previously
> > or is a naive
> > question.
> >
> > I have two samples A and B, and I want to test whether A
> > and B come from
> > the same distribution. The default Null hypothesis would be
> > H0: A=B
> > But since I am trying to prove that A and B indeed come
> > from the same
> > distribution, I think this is not the right choice for the
> > null hypothesis
> > (it should be one that is set up to be rejected)
> >
> > How do I specify a null hypothesis H0: A not equal to B for
> > say a KS test.
> > An example to do this in R would be greatly appreciated.
> >
> > On a related note: what is a good way to measure the
> > difference between
> > observed and expected PDFs? Is the D statistic of the KS
> > test a good choice?
> >
> > Thanks!
> > Nitin
> >
> >       [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> > http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained,
> > reproducible code.
>

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