the discussion of comparing variances brings to mind the following ... and
is related to the post i just sent re: hyp testing
let's assume that we are interested whether there is some difference in
treatment effects ... as measured by means ... our null is the mu1 = mu2
now, we use the 'standard' t test ... and forgive me, pooled variances ...
where the assumption is that this is a test of MEANS ... not differences in
variances ... so we assume equal variances.
but, given the data ... we suspect that there might be a difference in
variances so ... we do the (not preferred method as has been mentioned a
few times) classic F test ... first s square on top divided by second s
square on the bottom ....
HOWEVER ... as has been pointed out ... this test assumes for proper
interpretation that the populations are normally distributed ...
SOOOOOOOOOOOOOO ... given simple dotplots of the samples of data ... we
think that there could be some non normality going on here ...
SOOOOOOOOOOOO ... we look for a test of normality ... and of course, when
we find one, there will be assumptions for IT too
thus, what we are really interested is the difference in population means
... BUT, before we can look at this ... we have to check the equal variance
assumption ... BUT ... before we can look at this we ... need to check on
the normality assumption of IT ....
sort of a vicious circle
again ... if we had decided to abandon the notion of hypothesis testing ...
ALL of this flies out the window!!!
(is that windows 95??? or 98??? or 2000??? or ...WINNT???)
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