  Avalanches whose sizes and durations are distributed as power laws appear in
many contexts. Here, we show that there is a hidden peril in thresholding
continuous times series --either from empirical or synthetic data-- for the
detection of avalanches. In particular, we consider two possible alternative
definitions of avalanche size used e.g. in the empirical determination of
avalanche exponents in the analysis of neural-activity data. By performing
analytical and computational studies of an Ornstein-Uhlenbeck process (taken as
a guiding example) we show that if (i) relatively large threshold values are
employed to determine the beginning and ending of avalanches and if (ii) --as
sometimes done in the literature-- avalanche sizes are defined as the total
area (above zero) of the avalanche, then true asymptotic scaling behavior is
not seen, instead the observations are dominated by transient effects. These
can induce misinterpretations of the resulting scaling regimes as well as to a
wrong assignation of universality classes.
