Thanks Allin; ok, let's see if I understood your replies correctly:
1. The stand. dev. formula that is used in the 2-means test depends on
whether both samples are large (n>30) or not.
2. If one of the samples is "small", then a formula is used that appears
to be valid only in the case of equal variances.
3. there is no known/accepted way to test an arbitrary difference of
means in small samples with unequal (and unknown) variances
Provided those assessments are correct, I would suggest the following:
ad 1) I would like to see some mention of which assumption/formula is
used in the test output, or make it a user option.
ad 2) This is unfortunate given that the user explicitly choose the
option "do not assume equal variances". The easiest workaround is to use
the asymptotic formula instead and print a warning in the output that
the approximation may be poor due to the sample size.
ad 3) Given that insight, I think gretl should always use asymptotic
formulae for unequal variances and merely warn the user if samples are
small.
Ok, I expect that there are some details where I misunderstood Allin and
then some of these comments would probably be off mark. Please take that
into account.
I just discovered the test statistic calculator and I really like it,
especially for teaching. So I would like to make sure it's transparent
to the user what it does. (and hopefully does the right thing)
Thanks,
Sven
Allin Cottrell schrieb:
I wrote:
> Having checked with DeGroot, I stand by this value...
As for the case where we're _not_ assuming a constant variance and both
samples are large, I really don't know. In setting up gretl's
hypothesis test calculator I took a formula for the standard error of
the difference of means in this case from some statistics text (but
evidently not DeGroot).
Having read DeGroot and googled on the "Behrens-Fisher problem"
(hypothesis testing on the difference between two means, with the
repespective population means unknown and not assumed equal), the
case seems more or less hopeless. DeGroot says the problem is "very
difficult", and that while "various test procedures have been proposed",
"most of them have been the subject of controversy in regard to their
appropriateness or usefulness".
Allin Cottrell
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