  The realization that string theory gives rise to a huge landscape of vacuum
solutions has recently prompted a statistical approach towards extracting
phenomenological predictions from string theory. Unfortunately, for most
classes of string models, direct enumeration of all solutions is not
computationally feasible and thus statistical studies must resort to other
methods in order to extract meaningful information. In this paper, we discuss
some of the issues that arise when attempting to extract statistical
correlations from a large data set to which our computational access is
necessarily limited. Our main focus is the problem of ``floating
correlations''. As we discuss, this problem is endemic to investigations of
this type and reflects the fact that not all physically distinct string models
are equally likely to be sampled in any random search through the landscape,
thereby causing statistical correlations to ``float'' as a function of sample
size. We propose several possible methods that can be used to overcome this
problem, and we show through explicit examples that these methods lead to
correlations and statistical distributions which are not only stable as a
function of sample size, but which differ significantly from those which would
have been naively apparent from only a partial data set.
