  The detection of power-laws in real data is a demanding task for several
reasons. The two, more frequently met, being: (i) real data possess noise which
affects significantly the power-law tails and (ii) there is no solid tool for
the discrimination between a power-law, valid in a specific range of scales,
from other functional forms like log-normal or stretched exponential
distributions. In the present report we demonstrate, employing simulated and
real data, that using wavelets it is possible to overcome both of the above
mentioned difficulties and achieve a secure detection of a power-law and an
accurate estimation of the associated exponent.
