Hypothesis testing is a commonly used inference tool in many research fields. Null hypothesis significance testing (NHST) and its p-value In particular are ubiquitous in published research for decades. Much more recently, null hypothesis Bayesian testing (NHBT) and its Bayes factor also started to be more commonplace in applied research, especially in the social sciences. I have been interested in recent years in both the inferential properties of the Bayes factor and how researchers make use of it in applied research. In this presentation I will start by offering a (very) short overview of NHST and its shortcomings. I will then introduce the Bayes factor and discuss some of its properties. Importantly, I will focus on its advantages as well as on its shortcomings. Finally, I will present preliminary results of ongoing work, showing how social scientists have been misusing the Bayes factor in various ways. Very importantly, I will also ponder over the root causes of the identified problems, and I will provide suggestions to improve the current state of affairs.