I understand that Bartlett's Test is concerned with determining if your samples are from populations with equal variances.
If the samples are from populations with equal variances, then we fail to reject the null hypothesis of the test, and therefore a principal component analysis is inappropriate.
I'm not sure where the problem with this situation (having a homoskedastic data set) lies. What is the problem with having a data set where the underlying distribution of all your data is the same? I just don't see the big deal if this condition exists. Why would this make a PCA inappropriate?
I can't seem to find any good information anywhere online. Does anyone have any experience with interpreting why this test is relevant to a PCA?