Null hypothesis significance testing (NHST) has been under scrutiny for decades. The literature shows overwhelming evidence of a large range of problems affecting NHST. One of the proposed alternatives to NHST is using Bayes factors instead of p values. Here we denote the method of using Bayes factors to test point null models as ‘null hypothesis Bayesian testing’ (NHBT). In this article we offer a wide overview of potential issues (limitations or sources of misinterpretation) with NHBT which is currently missing in the literature. We illustrate many of the shortcomings of NHBT by means of reproducible examples. The article concludes with a discussion of NHBT in particular and testing in general. In particular, we argue that posterior model probabilities should be given more emphasis than Bayes factors, because only the former provide direct answers to the most common research questions under consideration.