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The q-Gaussians are a class of stable distributions which are present in manyscientific fields, and that behave as heavy tailed distributions for anespecific range of q values. The identification of these values, which are usedin the description of systems, is sometimes a hard task. In this work theidentification of a q-Gaussian distribution from empirical data was done by ameasure of its tail weight using robust statistics. Numerical methods were usedto generate artificial data, to find out the tail weight -- medcouple, and alsoto adjust the curve between medcouple and the q value. We showed that themedcouple value remains unchanged when the calculation is applied to data whichhave long memory. A routine was made to calculate the q value and its standarddeviation, when applied to empirical data. It is possible to identify aq-Gaussian by the proposed methods with higher precision than in the literaturefor the same data sample, or as precise as found in the literature. However, inthis case, it is required a smaller sample of data. We hope that this methodwill be able to open new ways for identifying physical phenomena that belongsto nonextensive frameworks.

Alternative way to characterize a q-gaussian distribution by a robust heavy tail measurement