The procedure of Fisher's null hypothesis test was already discussed briefly in the foregoing. Let \(h\) be the hypothesis of interest and, for the sake of simplicity, let \(S\) be a finite sample space. The hypothesis \(h\) imposes a distribution over the sample space, denoted \(P_{h}\). Every point \(s\) in the space represents a possible sample of data. We now define a function \(F\) on the sample space that identifies when we will reject the null hypothesis by marking the samples \(s\) that lead to rejection with \(F(s) = 1\), as follows:   \[   F(s) =   \begin{cases}  1 \quad \text{if } P_{h}(s) &lt; r,\\ 0 \quad \text{otherwise.} \end{cases} \]  Notice that the definition of the region of rejection, \(R_{r} = \{ s:\: F(s) = 1 \}\), hinges on the probability of the data under the assumption of the hypothesis, \(P_{h}(s)\). This expression is often called the likelihood of the hypothesis on the sample \(s\). We can set the threshold \(r\) for the likelihood to a suitable value, such that the total probability of the region of rejection \(R_{r}\) is below a given level of error, for example, \(P_{h}(R) &lt; 0.05\).