what probability a given deviation from the average should occur for a sequence that is genuinely chosen at random.

The result turns out to be quite consistent with what we see in pictures (g) and (h). But it is far from what we see in pictures (e) and (f). So even though individual block frequencies seem to suggest that sequences (d) and (e) are random, the lack of any spread in these frequencies provides evidence that in fact they are not.

So are sequences (g) and (h) in the end truly random? Just like other sequences discussed in this chapter they are in some sense not, since they can both be generated by simple underlying rules. But what the picture on the facing page demonstrates is that if one just does statistical analysis by computing frequencies of blocks one will see no evidence of any such underlying simplicity.

One might imagine that if one were to compute other quantities one could immediately find such evidence. But it turns out that many of the obvious quantities one might consider computing are in the end equivalent to various combinations of block frequencies. And perhaps as a result of this, it has sometimes been thought that if one could just compute frequencies of blocks of all lengths one would have a kind of universal test for randomness. But sequences like (e) and (f) on the facing page make it clear that this is not the case.

So what kinds of quantities can one in the end use in doing statistical analysis? The answer is that at least in principle one can use any quantity whatsoever, and in particular one can use quantities that arise from any of the processes of perception and analysis that I have discussed so far in this chapter. For in each case all one has to do is to compute the value of a quantity from a particular sequence of data, and then compare this value with what would be obtained by averaging over all possible sequences. In practice, however, the kinds of quantities actually used in statistical analysis of sequences tend to be rather limited. Indeed, beyond block frequencies, the only other ones that are common are those based on correlations, spectra, and occasionally run lengths—all of which we already discussed earlier in this chapter.

Nevertheless, one can in general imagine taking absolutely any process and using it as the basis for statistical analysis. For given some