It has been proposed that more use should be made of Bayes factors in hypothesis testing. Bayes factors are the ratios of the likelihood of a specified hypothesis (e.g. an intervention effect within a given range) to another hypothesis (e.g. no effect). They are particularly important for differentiating lack of strong evidence for an effect and evidence for lack of an effect. In a recent paper my research team reviewed randomized trials reported in the journal Addiction between January and June 2013 to assess how far Bayes factors might improve the interpretation of the data. We concluded that "Use of Bayes factors when analysing data from randomized trials of interventions in addiction research can provide important information that would lead to more precise conclusions than are obtained typically using currently prevailing methods".
Using BAYES FACTORS
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