 In the context of the Bayesian (see entry on  Bayes’ theorem)  approach to reasoning that was first developed by Mike Oaksford and Nick Chater in the 1980s (Oaksford &amp; Chater 2018), Hahn and colleagues have extended the Bayesian framework to the investigation of argumentation. They claim that Bayesian probabilities offer an accurate descriptive model of how people evaluate the strength of arguments (Hahn &amp; Oaksford 2007) as well as a solid perspective to address normative questions pertaining to argument strength (Hahn &amp; Oaksford 2006; Hahn &amp; Hornikx 2016). The Bayesian approach allows for the formulation of probabilistic measures of argument strength, showing that many so-called “fallacies” may nevertheless be good arguments in the sense that they considerably raise the probability of the conclusion. For example, deductively invalid argument schemes (such as affirming the consequent (AC) and denying the antecedent (DA)) can also provide considerable support for a conclusion, depending on the contents in question. The extent to which this is the case depends primarily on the specific informational context, captured by the prior probability distribution, not on the structure of the argument. This means that some instances of, say, AC, may offer support to a conclusion while others may fail to do so (Eva &amp; Hartmann 2018). Thus seen, Bayesian argumentation represents a significantly different approach to argumentation from those inspired by logic (e.g., argument schemes), but they are not necessarily incompatible; they may well be complementary perspectives (see also [Zenker 2013]).