  The Gini coefficient, a non-parametric measure of galaxy morphology, has
recently taken up an important role in the automated identification of galaxy
mergers. I present a critical assessment of its stability, based on a
comparison of HST/ACS imaging data from the GOODS and UDF surveys. Below a
certain signal-to-noise level, the Gini coefficient depends strongly on the
signal-to-noise ratio, and thus becomes useless for distinguishing different
galaxy morphologies. Moreover, at all signal-to-noise levels the Gini
coefficient shows a strong dependence on the choice of aperture within which it
is measured. Consequently, quantitative selection criteria involving the Gini
coefficient, such as a selection of merger candidates, cannot always be
straightforwardly applied to different datasets. I discuss whether these
effects could have affected previous studies that were based on the Gini
coefficient, and establish signal-to-noise limits above which measured Gini
values can be considered reliable.
