Central to the above discussion on classical statistics is the concept of likelihood, which reflects how the data bears on the hypotheses at issue. In the works of Hacking (1965), Edwards (1972), and more recently Royall (1997), the likelihoods are taken as a cornerstone for statistical procedures and given an epistemic interpretation. They are said to express the strength of the evidence presented by the data, or the comparative degree of support that the data give to a hypothesis. Hacking formulates this idea in the so-called law of likelihood (1965, p. 59): if the sample \(s\) is more probable on the condition of \(h_{0}\) than on \(h_{1}\), then \(s\) supports \(h_{0}\) more than it supports \(h_{1}\).