  Using mock spectra based on Vazdekis/MILES library fitted within the
wavelength region 3600-7350\AA, we analyze the bias and scatter on the
resulting physical parameters induced by the choice of fitting algorithms and
observational uncertainties, but avoid effects of those model uncertainties. We
consider two full-spectrum fitting codes: pPXF and STARLIGHT, in fitting for
stellar population age, metallicity, mass-to-light ratio, and dust extinction.
With pPXF we find that both the bias in the population parameters and the
scatter in the recovered logarithmic values follows the expected trend. The
bias increases for younger ages and systematically makes recovered ages older,
$M_*/L_r$ larger and metallicities lower than the true values. For reference,
at S/N=30, and for the worst case ($t=10^8$yr), the bias is 0.06 dex in
$M_*/L_r$, 0.03 dex in both age and [M/H]. There is no significant dependence
on either E(B-V) or the shape of the error spectrum. Moreover, the results are
consistent for both our 1-SSP and 2-SSP tests. With the STARLIGHT algorithm, we
find trends similar to pPXF, when the input E(B-V)<0.2 mag. However, with
larger input E(B-V), the biases of the output parameter do not converge to zero
even at the highest S/N and are strongly affected by the shape of the error
spectra. This effect is particularly dramatic for youngest age, for which all
population parameters can be strongly different from the input values, with
significantly underestimated dust extinction and [M/H], and larger ages and
$M_*/L_r$. Results degrade when moving from our 1-SSP to the 2-SSP tests. The
STARLIGHT convergence to the true values can be improved by increasing Markov
Chains and annealing loops to the "slow mode". For the same input spectrum,
pPXF is about two order of magnitudes faster than STARLIGHT's "default mode"
and about three order of magnitude faster than STARLIGHT's "slow mode".
