 In nonstatistical learning, we required a reliable method to eventually settle on true hypothesis after sufficiently many observations. The statistical version of this criterion is that after sufficiently many observations, the chance of conjecturing the true hypothesis should approach 100%. More technically, say that a method identifies a true statistical hypothesis in chance if for every bias value \(p\), and for every threshold \(0\lt t \lt 1\), there is a sample size \(n\) such that for all larger sample sizes, the method conjectures the hypothesis \(H\) that is true for \(p\) with at least probability \(t\). In symbols, we have \(P_{n',p}(H) \gt t\) for all sample sizes \(n' \gt n\), where \(H\) is the hypothesis that is true for \(p\). The figure below illustrates how the chance of conjecturing the true hypothesis increases with sample size, whereas the chance of conjecturing a false hypothesis decreases with sample size. The definition can be generalized to more complex statistical hypotheses by replacing the true bias value \(p\) with a list of parameters.