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tokens: the observed acceleration g obs to that due to baryons g bar , where , , and g † is the only free parameter. This relation was established by analyzing the data points from 153 galaxies in a statistical sense. We wanted to test the constancy of G N , so we fit Eq. ( 2 ) to rotation curves fixing g † = 1.2 × 10 −10 m s −2 and varying G N from galaxy to galaxy. We also tried to vary g † and G N simultaneously, and obtained similar results. We show as an example the case that has the same number of fitting parameters as in Rodrigues et al. (2018a) . We set the likelihood function as with χ 2 given by where g RAR is the expected centripetal acceleration from Eq. ( 2 ) and is the uncertainty on the observed acceleration. We impose a flat prior on G N with G N > 0 as Rodrigues et al. (2018a) did for g † . Following the procedure of Rodrigues et al. (2018a) , we also impose flat priors on Υ ⋆ with a tolerance of a factor of two, though this is not consistent with stellar population synthesis models ( Schombert et al. 2019 ). Galaxy distance affects the fit quality as it is directly related to the contributions of the baryonic components. When the distance D is changed to D ′, the galaxy radius R and the contribution of each component will transform according to where k denotes gas, disk, or bulge. Rodrigues et al. (2018a) allow galaxy distance to vary freely within 20% of their observational values. This prior does not reflect actual observational errors. Distances measured through Cepheids or the tip magnitude of the red giant branch are highly accurate, so a 20% variation could be larger than 4 σ . The Hubble flow method is much less accurate; distances measured using this method could have errors up to 30%, so the imposed 20% free range is even within the 1 σ region. Rodrigues et al. (2018a) also ignored the uncertainty on disk inclination. Possible outer asymmetries and warps in the gas disk could mislead the determination of inclinations, as reflected in their uncertainties, which would in turn affect the observed rotation velocities. Despite the shortcomings of the methodology of Rodrigues et al. (2018a) , we choose to fit the same parameters (except G N instead of g † ) and impose the same priors to achieve the most direct comparison. We map the posterior distributions of the fitting parameters using the standard affine-invariant ensemble sampler in the Python package e m c e e ( Foreman-Mackey et al. 2013 ). The Markov chains are initialized with 200 random walkers and iterated for 2000 steps after 1000 burnt-in iterations. We record the best-fit parameters that maximize the probability function for every galaxy. Figure 1 shows the posterior probability distribution of G N . Though we analyze all the SPARC galaxies, we only include 101 of them in Fig. 1 based on the following quality cuts: we removed galaxies with quality flag Q = 3 (see Lelli et al. 2016 ), inclination smaller than 30°, or reduced . This is a quality cut similar to that