Complex nonequilibrium systems are often effectively described by a
`statistics of a statistics', in short, a `superstatistics'. We describe how to
proceed from a given experimental time series to a superstatistical
description. We argue that many experimental data fall into three different
universality classes: chi^2-superstatistics (Tsallis statistics), inverse
chi^2-superstatistics, and log-normal superstatistics. We discuss how to
extract the two relevant well separated superstatistical time scales tau and T,
the probability density of the superstatistical parameter beta, and the
correlation function for beta from the experimental data. We illustrate our
approach by applying it to velocity time series measured in turbulent
Taylor-Couette flow, which is well described by log-normal superstatistics and
exhibits clear time scale separation.Comment: 7 pages, 9 figure
