  The surrogate data method is widely applied as a data dependent technique to
test observed time series against a barrage of hypotheses. However, often the
hypotheses one is able to address are not those of greatest interest,
particularly for system known to be nonlinear. In the review we focus on
techniques which overcome this shortcoming. We summarize a number of recently
developed surrogate data methods. While our review of surrogate methods is not
exhaustive, we do focus on methods which may be applied to experimental, and
potentially nonlinear, data. In each case, the hypothesis being tested is one
of the interests to the experimental scientist.
