Date: Mon, 12 Oct 2009 09:49:49 +0200 =?GB2312?B?R2FyY6iqYS1HcmFuZXJv?= "SPSSX(r) Discussion" =?GB2312?B?R2FyY6iqYS1HcmFuZXJv?= Re: T-test Clarification text/plain; charset=GB2312

Hi Dorraj

DorraJ Oet wrote: > > I need clarification for something basic. > > We know that 2 independent samples T-test has the following assumptions > > 1. The scale follows normal distribution. > 2. The 2 categories in the categorical variable is mutually exclusive. > 3. The homogenity of variance assumption has to be met. > > > My question is as follows > > 1. Assuming that the normality is met, what happen if the > homogenity of variance assumption failed? Do we go to a > non-parametric test say Mann Whitney or do we just use the > variance not assumed T-test Statistics in the T-test table? >

Although not a lot of people knows that, Mann-Whitney's test is very sensitive to differences in spread (heterogeneity of variances). Therefore, it is as bad as a T-test when homogeneity of variances is not met. You must use the "variances not assumed" (Welch test is the name, BTW) T-test. > > 1. I had a professor once commented that he will use the variance > not assumed T-test Statistics and that Statistics is the > calculation when the variance is not pooled but for separated > categories. He even mentioned that going from Parametric to > Non-parametrice approach is not correct. Is he right by > commenting these? >

Yes, he is indeed (see above). "Distribution-free" is a misnomer. Non-parametric tests can have some conditions concerning the distribution of the data. For instance, signed ranks Wilcoxon test needs symmetrical distributions, Mann-Whitney's U and Kruskal-Wallis tests are sensitive to differences in shape and or spread of the groups being compared...

HTH, Prof. Marta Garc¨Şa-Granero

-- For miscellaneous SPSS related statistical stuff, visit: http://gjyp.nl/marta/

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