ABSTRACT
When applying a permutation or bootstrap test, it is not necessary to de-
termine the theoretical distribution of the test statistic. This enables great
flexibility. For instance, test statistics can be used that are extremely difficult,
or even impossible, to handle analytically. Moreover, very complex designs
can be analyzed. As a consequence, permutation and bootstrap tests might
be the method of choice for nonstandard situations and complex designs. Ac-
cording to Manly (2007, p. 341), it is one of the most important advantages
of computer-intensive methods such as bootstrap and permutation tests, that
they can be applied for data that do not fit into any of the usual categories.
Manly (2007) presented four examples, including a study of Cushman et al.
(1993) who investigated whether the size of European ants depends on the
latitude (see Manly, 2007, pp. 365-369).
