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The distribution of errors is a central object in the assesment andbenchmarking of computational chemistry methods. The popular and often blinduse of the mean unsigned error as a benchmarking statistic leads to ignoredistributions features that impact the reliability of the tested methods. Weexplore how the Gini coefficient offers a global representation of the errorsdistribution, but, except for extreme values, does not enable an unambiguousdiagnostic. We propose to relieve the ambiguity by applying the Ginicoefficient to mode-centered error distributions. This version can usefullycomplement benchmarking statistics and alert on error sets with potentiallyproblematic shapes.

Using the Gini coefficient to characterize the shape of computational chemistry error distributions