Or is there some other reason? Second, it is alleged this technique is scale-invariant (wikipedia) but my practical experience is this may well only be doable with Gaussian facts and that since genuine info is generally not Gaussian distributed, scale-variance function would not keep? You should comment. Thanks
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The greater interesting image may be the geometry in the Cholesky transformation, which standardizes and "uncorrelates" the variables.
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My query is: could it be valid to check Mahalanobis distances which were produced using diverse reference distributions? I've only ever witnessed it used to match check observations relative to one typical reference distribution.
As said in your report 'Tests info for multivariate normality', the squared Mahalanobis length has an approximate chi-squared distribution when the information are MVN.