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" <[EMAIL PROTECTED]> 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? > > รก =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@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. > ______________________________________________ R-help@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.