  The method of surrogate data provides a framework for testing observed data
against a hierarchy of alternative hypotheses. The aim of applying this method
is to exclude the possibility that the data are consistent with simple linear
explanations before seeking complex nonlinear causes. However, in recent time
the method has attracted considerable criticism, largely as a result of
ambiguity about the formation of the underlying null hypotheses, or about the
power of the chosen statistic. In this communication we show that by employing
a special family of ranks statistics these problems can be avoided and the
method of surrogate data placed of a firm statistical foundation.
