 More recently, better probabilistic accounts of corroboration have been developed (Fenton and Neil 2013 [2018]; Robertson, Vignaux, and Berger 2016; Taroni et al. 2014). A recurrent theme in this line of work is that corroboration can be accounted for in probabilistic terms by multiplying the likelihood ratios of the individual pieces of evidence. The thought is that the result of multiplying the likelihood ratios of different pieces of evidence combined exceeds the likelihood ratios associated with the individual pieces of evidence (see general discussion in Bovens and Hartmann 2004: ch. 5). Cohen, however, insisted that the confidence boost due to corroboration should be large, and the large boost is not reflected as a result of multiplying the likelihood ratios. What needs further exploring is the size of the confidence boost and the features of the evidence that affect the boost. Urbaniak and Janda (2020) offer a detailed discussion of Cohen’s objections and candidates for a solution.