  We study the statistical properties of the Normalized Excess Variance of
variability process characterized by a red-noise power spectral density (PSD),
as the case of Active Galactic Nuclei (AGN). We perform Monte Carlo simulations
of lightcurves, assuming both a continuous and a sparse sampling pattern and
various signal-to-noise (S/N) ratios. We show that the normalized excess
variance is a biased estimate of the variance even in the case of continuously
sampled lightcurves. The bias depends on the PSD slope and on the sampling
pattern, but not on the S/N ratio. We provide a simple formula to account for
the bias, which yields unbiased estimates with an accuracy better than 15%. We
show that the normalized excess variance estimates based on single lightcurves
(especially for sparse sampling and S/N less than 3) are highly uncertain (even
if corrected for bias) and we propose instead the use of an ensemble estimate,
based on multiple lightcurves of the same object, or on the use of lightcurves
of many objects. These estimates have symmetric distributions, known errors,
and can also be corrected for biases.We use our results to estimate the ability
to measure the intrinsic source variability in current data, and show that they
could also be useful in the planning of the observing strategy of future
surveys such as those provided by X-ray missions studying distant and/or faint
AGN populations and, more in general, in the estimation of the variability
amplitude of sources that will result from future surveys such as Pan-STARRS,
and LSST.
