  Consider the situation where a word is chosen probabilistically from a finite
list. If an attacker knows the list and can inquire about each word in turn,
then selecting the word via the uniform distribution maximizes the attacker's
difficulty, its Guesswork, in identifying the chosen word. It is tempting to
use this property in cryptanalysis of computationally secure ciphers by
assuming coded words are drawn from a source's typical set and so, for all
intents and purposes, uniformly distributed within it. By applying recent
results on Guesswork, for i.i.d. sources it is this equipartition ansatz that
we investigate here. In particular, we demonstrate that the expected Guesswork
for a source conditioned to create words in the typical set grows, with word
length, at a lower exponential rate than that of the uniform approximation,
suggesting use of the approximation is ill-advised.
