In article <a4heq0$ki3$[EMAIL PROTECTED]>,
Matthias <[EMAIL PROTECTED]> wrote:
>Hello,
>would be nice if someone can give me some advice with regard to the
>following problem:
>I would like to compare the means of two independent numerical sets of data
>whether they are significantly different from each other or not. One of the
>two underlying assumption to calculate the T-Test is not given (Variances
>are assumed to be NOT equally distributed; but data is normally
>distributed). What kind of (non?)parametric-test does exist - instead of the
>T-Test - to calculate possible differences in the two means?
>I'm using SPSS for further calculations.
Do you really want to compare the MEANS? If so there are NO
"non-parametric" tests, unless you are willing to make some
other assumptions, such as symmetry, and you are comparing
the centers of the distributions. However, most of these
tests also detect different distributions, possibly not as
well.
There is a simple randomized solution of the problem,
assuming normality. It loses degrees of freedom to one less
than that of the smaller sample. One can separate the means
from orthogonal linear combinations of the observations with
mean 0 and the same variance as the original observations;
this does not even need normality. Then the mean square of
the appropriate linear combinations of them will give the
needed estimate of the variance of the difference of the means.
--
This address is for information only. I do not claim that these views
are those of the Statistics Department or of Purdue University.
Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399
[EMAIL PROTECTED] Phone: (765)494-6054 FAX: (765)494-0558
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