 While the results in this section establish a fruitful connection between simplicity and mind-change optimality, a limitation of the approach is that it requires that some hypotheses must be conclusively entailed or falsified by some evidence sequence. This is typically not the case for statistical models, where the probability of a hypothesis may become arbitrarily small but usually not 0. For instance, consider a coin flip problem and the hypothesis “the probability of heads is 90%”. If we observe one million tails, the probability of the hypothesis is very small indeed, but it is not 0, because any number of tails is logically consistent with a high probability of heads. The next section discusses how a reliabilist approach can be adapted to statistical hypotheses.