  A quantitative evaluation of the influence of sampling on the numerical
fractal analysis of experimental profiles is of critical importance. Although
this aspect has been widely recognized, a systematic analysis of the sampling
influence is still lacking. Here we present the results of a systematic
analysis of synthetic self-affine profiles in order to clarify the consequences
of the application of a poor sampling (up to 1000 points) typical of Scanning
Probe Microscopy for the characterization of real interfaces and surfaces. We
interprete our results in term of a deviation and a dispersion of the measured
exponent with respect to the ``true'' one. Both the deviation and the
dispersion have always been disregarded in the experimental literature, and
this can be very misleading if results obtained from poorly sampled images are
presented. We provide reasonable arguments to assess the universality of these
effects and we propose an empirical method to take them into account. We show
that it is possible to correct the deviation of the measured Hurst exponent
from the 'true' one and give a reasonable estimate of the dispersion error. The
last estimate is particularly important in the experimental results since it is
an intrinsic error that depends only on the number of sampling points and can
easily overwhelm the statistical error. Finally, we test our empirical method
calculating the Hurst exponent for the well-known 1+1 dimensional directed
percolation profiles, with a 512-point sampling.
