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Abstract Massively multiplexed spectrographs will soon gather large statistical samples of stellar spectra. The accurate estimation of uncertainties on derived parameters, such as the line-of-sight velocityv_los, especially for spectra with low signal-to-noise ratios (S/Ns), is paramount. We generated an ensemble of simulated optical spectra of stars as if they were observed with low- and medium-resolution fiber-fed instruments on an 8 m class telescope, similar to the Subaru Prime Focus Spectrograph, and determinedv_losby fitting stellar templates to the simulated spectra. We compared the empirical errors of the derived parameters—calculated from an ensemble of simulations—to the asymptotic errors determined from the Fisher matrix, as well as from Monte Carlo sampling of the posterior probability. We confirm that the uncertainty ofv_losscales with the inverse square root of the S/N, but also show how this scaling breaks down at low S/N and analyze the error and bias caused by template mismatch. We outline a computationally optimized algorithm to fit multiexposure data and provide a mathematical model of stellar spectrum fitting that maximizes the so called significance, which allows for calculating the error from the Fisher matrix analytically. We also introduce the effective line count, and provide a scaling relation to estimate the errors ofv_losmeasurements based on stellar type. Our analysis covers a range of stellar types with parameters that are typical of the Galactic outer disk and halo, together with analogs of stars in M31 and in satellite dwarf spheroidal galaxies around the Milky Way.