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Another illustration of the same general point is provided by the numerous statistical procedures (factor analysis, cluster analysis, multidimensional scaling techniques) that allow one to summarize or represent large bodies of statistical information in an economical, unified way and to derive more specific statistical facts from a much smaller set of assumptions by repeated use of the same pattern of argument. For example, knowing the “loading” of each of n intelligence tests on a single Equation editor common factor g, one can derive a much larger number (n(n-1)/2) of conclusions about pairwise correlations among these tests. Again, however, it is doubtful that by itself this “unification” tells us anything about the causes of performance on these tests.
Another fundamental difficulty with the unificationist account derives from its reliance on what might be called a “winner take all” conception of unification. On the one hand, it seems that any plausible version of that account must yield the conclusion that generalizations and theories can sometimes be explanatory with respect to some set of phenomena even though more unifying Scientific software explanations of those phenomena are known[ 18 ]. For example, Galileo's law can be used to explain facts about the behavior of falling bodies even though it furnishes a less unifying explanation than the laws of Newtonian mechanics and gravitational theory, the latter are in turn explanatory even though the explanations they provide are less unified than those provided by General Relativity, the theories of Coulomb and Ampere are explanatory even though the explanations they provide are less unified than the explanations provided by Maxwell's theory, and so on. If we reject this idea, we must adopt the conclusion that in any domain only the most unified theory that is known is explanatory at all; everything else is non-explanatory. Call this the winner-take-all conception of explanatory unification.
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