# [R] small sample techniques

Thomas Lumley tlumley at u.washington.edu
Wed Aug 8 17:04:10 CEST 2007

On Wed, 8 Aug 2007, Nair, Murlidharan T wrote:

> 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?
>

Yes. You are probably looking for the version of the t.test that assumes equal variances (the original one), so you need var.equal=TRUE.

-thomas

> -----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
>>
>>
>>
>> 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]]
>>
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>
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