[R] small sample techniques

[Nair, Murlidharan T]

Indeed, I understand what you say. The df of freedom for the dummy example is n1+n2-2 = 8. But when I run the t.test I get it as 5.08, am I missing something? 

-----Original Message-----
From: Moshe Olshansky [mailto:m_olshansky at yahoo.com] 
Sent: Tuesday, August 07, 2007 9:05 PM
To: Nair, Murlidharan T; r-help at stat.math.ethz.ch
Subject: Re: [R] small sample techniques

Hi Nair,

If the two populations are normal the t-test gives you
the exact result for whatever the sample size is (the
sample size will affect the number of degrees of
freedom).
When the populations are not normal and the sample
size is large it is still OK to use t-test (because of
the Central Limit Theorem) but this is not necessarily
true for the small sample size.
You could use simulation to find the relevant
probabilities.

--- "Nair, Murlidharan T" <mnair at iusb.edu> wrote:

> If my sample size is small is there a particular
> switch option that I need to use with t.test so that
> it calculates the t ratio correctly? 
> 
> Here is a dummy example?
> 
> á =0.05
> 
> Mean pain reduction for A =27; B =31 and SD are
> SDA=9 SDB=12
> 
> drgA.p<-rnorm(5,27,9); 
> 
> drgB.p<-rnorm(5,31,12)
> 
> t.test(drgA.p,drgB.p) # what do I need to give as
> additional parameter here?
> 
>  
> 
> I can do it manually but was looking for a switch
> option that I need to specify for  t.test. 
> 
>  
> 
> Thanks ../Murli
> 
>  
> 
> 
> 	[[alternative HTML version deleted]]
> 
> > ______________________________________________
> R-help at stat.math.ethz.ch 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.
>