The cognitive processes underlying Go/No-Go performance may be explained by two plausible evidence accumulation models: Two-Boundary (2-B) and One-Boundary (1-B) decision drift models (DDMs). While both embed a Go decision, the 2-B DDM embeds a definitive No-Go decision, whereas the 1-B DDM embeds a response window for Go. Using simulations, we found that model comparison methods like leave-one-out cross-validation (LOO), coupled with Bayesian hierarchical modeling, can correctly identify the underlying model. Additionally, using the correct model reduces the risk of missing true effects or detecting spurious findings. Therefore, we recommend researchers implement and compare both models for Go/No-Go studies to reduce misleading results. Lastly, we implemented these models to investigate race effects in the decision to shoot during police training. We found that the accumulated evidence needed to reach the Shoot decision is lower for Black suspects, which explains the heightened error rates for shooting unarmed Black suspects in data.