  The q-Gaussians are a class of stable distributions which are present in many
scientific fields, and that behave as heavy tailed distributions for an
especific range of q values. The identification of these values, which are used
in the description of systems, is sometimes a hard task. In this work the
identification of a q-Gaussian distribution from empirical data was done by a
measure of its tail weight using robust statistics. Numerical methods were used
to generate artificial data, to find out the tail weight -- medcouple, and also
to adjust the curve between medcouple and the q value. We showed that the
medcouple value remains unchanged when the calculation is applied to data which
have long memory. A routine was made to calculate the q value and its standard
deviation, when applied to empirical data. It is possible to identify a
q-Gaussian by the proposed methods with higher precision than in the literature
for the same data sample, or as precise as found in the literature. However, in
this case, it is required a smaller sample of data. We hope that this method
will be able to open new ways for identifying physical phenomena that belongs
to nonextensive frameworks.
