  Pareto distributions, and power laws in general, have demonstrated to be very
useful models to describe very different phenomena, from physics to finance. In
recent years, the econophysical literature has proposed a large amount of
papers and models justifying the presence of power laws in economic data. Most
of the times, this Paretianity is inferred from the observation of some plots,
such as the Zipf plot and the mean excess plot. If the Zipf plot looks almost
linear, then everything is ok and the parameters of the Pareto distribution are
estimated. Often with OLS. Unfortunately, as we show in this paper, these
heuristic graphical tools are not reliable. To be more exact, we show that only
a combination of plots can give some degree of confidence about the real
presence of Paretianity in the data. We start by reviewing some of the most
important plots, discussing their points of strength and weakness, and then we
propose some additional tools that can be used to refine the analysis.
