  The distribution of errors is a central object in the assesment and
benchmarking of computational chemistry methods. The popular and often blind
use of the mean unsigned error as a benchmarking statistic leads to ignore
distributions features that impact the reliability of the tested methods. We
explore how the Gini coefficient offers a global representation of the errors
distribution, but, except for extreme values, does not enable an unambiguous
diagnostic. We propose to relieve the ambiguity by applying the Gini
coefficient to mode-centered error distributions. This version can usefully
complement benchmarking statistics and alert on error sets with potentially
problematic shapes.
