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Using mock spectra based on Vazdekis/MILES library fitted within thewavelength region 3600-7350\AA, we analyze the bias and scatter on theresulting physical parameters induced by the choice of fitting algorithms andobservational uncertainties, but avoid effects of those model uncertainties. Weconsider two full-spectrum fitting codes: pPXF and STARLIGHT, in fitting forstellar population age, metallicity, mass-to-light ratio, and dust extinction.With pPXF we find that both the bias in the population parameters and thescatter in the recovered logarithmic values follows the expected trend. Thebias 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 dependenceon either E(B-V) or the shape of the error spectrum. Moreover, the results areconsistent for both our 1-SSP and 2-SSP tests. With the STARLIGHT algorithm, wefind trends similar to pPXF, when the input E(B-V)<0.2 mag. However, withlarger input E(B-V), the biases of the output parameter do not converge to zeroeven at the highest S/N and are strongly affected by the shape of the errorspectra. This effect is particularly dramatic for youngest age, for which allpopulation parameters can be strongly different from the input values, withsignificantly 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. TheSTARLIGHT convergence to the true values can be improved by increasing MarkovChains 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".

Recovering stellar population parameters via two full-spectrum fitting algorithms in the absence of model uncertainties