  However, we needn’t use such an extreme example to make the point. For the same phenomenon exists with almost any unrepresentative outcome sequence. A fair coin, tossed 1000 times, has a positive chance of landing heads more than 700 times. But any outcome sequence of 1000 tosses which contains more than 700 heads will be compressible (long runs of heads are common enough to be exploited by an efficient coding algorithm, and 1000 outcomes is long enough to swamp the constants involved in defining the universal prefix-free Kolmogorov complexity). So any such outcome sequence will not be random, even though it quite easily could come about by chance. The only way to resist this counterexample is to refuse to acknowledge that such a sequence of outcomes can be an appropriate sequence in RCT. This is implausible, for such sequences can be actual, and can be sufficiently long to avoid the analogue of the problem of the single case, certainly long enough for the Kolmogorov definition of randomness to apply. The only reason to reject such sequences as suitable is to save RCT, but that is clearly question begging in this context. In this case of an unrepresentative finite sequence, even reductionism about chance needn’t help, because it might be that other considerations suffice to fix the chance, and so we can have a genuine fair chance but a biased and non-random outcome sequence.