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,
> 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
Prof. Marta Garc¨Şa-Granero
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