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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.