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The era of the James Webb Space Telescope ushers stellar population modelsinto uncharted territories, particularly at the high-redshift frontier. In acompanion paper, we apply the \texttt{Prospector} Bayesian framework to jointlyinfer galaxy redshifts and stellar population properties from broad-bandphotometry as part of the UNCOVER survey. Here we present a comprehensive errorbudget in spectral energy distribution (SED) modeling. Using a sample selectedto have photometric redshifts higher than 9, we quantify the systematic shiftsstemming from various model choices in inferred stellar mass, star formationrate (SFR), and age. These choices encompass different timescales for changesin the star formation history (SFH), non-universal stellar initial massfunctions (IMF), and the inclusion of variable nebular abundances, gas densityand ionizing photon budget. We find that the IMF exerts the strongest influenceon the inferred properties: the systematic uncertainties can be as much as 1dex, 2--5 times larger than the formal reported uncertainties in mass and SFR;and importantly, exceed the scatter seen when using different SED fittingcodes. Although the assumptions on the lower end of the IMF induce degeneracy,our findings suggest that a common practice in the literature of assessinguncertainties in SED-fitting processes by comparing multiple codes issubstantively underestimating the true systematic uncertainty. Highlystochastic SFHs change the inferred SFH by much larger than the formaluncertainties, and introduce $\sim 0.8$ dex systematics in SFR averaged overshort time scale and $\sim 0.3$ dex systematics in average age. Finally,employing a flexible nebular emission model causes $\sim 0.2$ dex systematicincrease in mass and SFR, comparable to the formal uncertainty. This paperconstitutes an initial step toward a complete uncertainty estimate in SEDmodeling.

Quantifying the Effects of Known Unknowns on Inferred High-redshift Galaxy Properties: Burstiness, the IMF, and Nebular Physics