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The realization that string theory gives rise to a huge landscape of vacuumsolutions has recently prompted a statistical approach towards extractingphenomenological predictions from string theory. Unfortunately, for mostclasses of string models, direct enumeration of all solutions is notcomputationally feasible and thus statistical studies must resort to othermethods in order to extract meaningful information. In this paper, we discusssome of the issues that arise when attempting to extract statisticalcorrelations from a large data set to which our computational access isnecessarily limited. Our main focus is the problem of ``floatingcorrelations''. As we discuss, this problem is endemic to investigations ofthis type and reflects the fact that not all physically distinct string modelsare equally likely to be sampled in any random search through the landscape,thereby causing statistical correlations to ``float'' as a function of samplesize. We propose several possible methods that can be used to overcome thisproblem, and we show through explicit examples that these methods lead tocorrelations and statistical distributions which are not only stable as afunction of sample size, but which differ significantly from those which wouldhave been naively apparent from only a partial data set.Comment: 29 pages, LaTeX, 8 figure

Fighting the floating correlations: Expectations and complications in extracting statistical correlations from the string theory landscape