  In light of recent discussions of the string landscape, it is essential to
understand the degree to which string theory is predictive. We argue that it is
unlikely that the landscape as a whole will exhibit unique correlations amongst
low-energy observables, but rather that different regions of the landscape will
exhibit different overlapping sets of correlations. We then provide a
statistical method for quantifying this degree of predictivity, and for
extracting statistical information concerning the relative sizes and overlaps
of the regions corresponding to these different correlation classes. Our method
is robust and requires no prior knowledge of landscape properties, and can be
applied to the landscape as a whole as well as to any relevant subset.
