  One response to this problem is to try and come up with a characterisation of randomness which will permit the outcomes of biased chances to be random. It is notable that von Mises’ initial characterisation of randomness was expressly constructed with this in mind—for him, a random sequence is one for which there is no admissible subsequence having a frequency differing from the frequency in the original sequence. This account is able to handle any value for the frequency, not only the case where each of two outcomes are equifrequent. Given that the Martin-Löf approach is a generalisation of von Mises’, it is not surprising that it too can be adapted to permit biased sequences to be random. Consider a binary process with outcome probabilities \((p, 1 - p)\). The law of large numbers in a general form tells us that a measure one set of sequences of independent trials of such a process will have limit frequencies of outcomes equal to \((p, 1 - p)\). This measure is not the standard Lebesgue measure, but rather a measure defined by the chance function in question. We can similarly re-interpret the other effective statistical tests of randomness. Drawing as we did above (§2.1.2) on the language of statistical testing, we can characterise the random sequences as those which are not significant with respect to the hypothesis that the outcome chances are \((p, 1 - p)\)—those which, as it were, conform to our prior expectations based on the underlying chances.