Bayes factors are used in cognitive sciences to compare different hypotheses based on data. They can help in making decisions about which hypothesis is more likely. However, Bayes factors can be sensitive to data details and computational methods. This study investigated how accurate Bayes factors are in different situations. The researchers found that errors can occur in estimating Bayes factors, and their accuracy can vary depending on the analysis method used. They also developed a workflow for researchers to test the robustness of Bayes factors in their own studies. This can help scientists understand the strengths and limitations of using Bayes factors to support their hypotheses in research.