 Alternatively, scientists can restrict themselves to a purely evidential interpretation of hypothesis tests and leave decisions to policy-makers and regulatory agencies. The statistician and biologist R.A. Fisher (1935, 1956) proposed what later became the orthodox quantification of evidence in frequentist statistics. Suppose a “null” or default hypothesis \(H_0\) denotes that an intervention has zero effect. If the observed data are “extreme” under \(H_0\)—i.e., if it was highly likely to observe a result that agrees better with \(H_0\)—the data provide evidence against the null hypothesis and for the efficacy of the intervention. The epistemological rationale is connected to the idea of severe testing (Mayo 1996): if the intervention were ineffective, we would, in all likelihood, have found data that agree better with the null hypothesis. The strength of evidence against \(H_0\) is equal to the \(p\)-value: the lower it is, the stronger evidence \(E\) speaks against the null hypothesis \(H_0\).