  Cosmic voids are large underdense regions that, together with galaxy
clusters, filaments and walls, build up the large-scale structure of the
Universe. The void size function provides a powerful probe to test the
cosmological framework. However, to fully exploit this statistics, the void
sample has to be properly cleaned from spurious objects. Furthermore, the bias
of the mass tracers used to detect these regions has to be taken into account
in the size function model. In our work we test a cleaning algorithm and a new
void size function model on a set of simulated dark matter halo catalogues,
with different mass and redshift selections, to investigate the statistics of
voids identified in a biased mass density field. We then investigate how the
density field tracers' bias affects the detected size of voids. The main result
of this analysis is a new model of the size function, parameterised in terms of
the linear effective bias of the tracers used, which is straightforwardly
inferred from the large-scale two-point correlation function. This represents a
crucial step to exploit the method on real data catalogues. The proposed size
function model has been accurately calibrated on mock catalogues, and used to
validate the possibility to provide forecasts on the cosmological constraints,
namely on the matter density contrast, $\Omega_{\rm M}$, and on the
normalisation of the linear matter power spectrum, $\sigma_8$, at different
redshifts.
