Response inhibition is a widely studied aspect of cognitive control that is particularly interesting because of its applications to clinical populations. Although individual differences are integral to cognitive control, so too is our ability to aggregate information across a group of individuals, so that we can powerfully generalize and characterize the group's behavior. Hence, an examination of response inhibition would ideally involve an accurate estimation of both group- and individual-level effects. Hierarchical Bayesian analyses account for individual differences by simultaneously estimating group and individual factors and compensate for sparse data by pooling information across participants. Hierarchical Bayesian models are thus an ideal tool for studying response inhibition, especially when analyzing neural data. We construct hierarchical Bayesian models of the fMRI neural time series, models assuming hierarchies across conditions, participants, and ROIs. Here, we demonstrate the advantages of our models over a conventional generalized linear model in accurately separating signal from noise. We then apply our models to go/no-go and stop signal data from 11 participants. We find strong evidence for individual differences in neural responses to going, not going, and stopping and in functional connectivity across the two tasks and demonstrate how hierarchical Bayesian models can effectively compensate for these individual differences while providing group-level summarizations. Finally, we validated the reliability of our findings using a larger go/no-go data set consisting of 179 participants. In conclusion, hierarchical Bayesian models not only account for individual differences but allow us to better understand the cognitive dynamics of response inhibition.