Bayes factors are used in cognitive sciences to compare different hypotheses based on data. However, they can be influenced by data/model assumptions and computational details like bridge sampling. The study found errors in Bayes factor estimation, tested their accuracy using simulation-based calibration, checked their stability against different data samples, and looked at decision variability based on Bayes factors. A workflow was outlined for researchers to assess the robustness of Bayes factors in their analyses.