  Studentisation upon rank-based linear estimators is generally considered an
unnecessary topic, due to the domain restriction upon $S_{n}$, which exhibits
constant variance. This assertion is functionally inconsistent with general
analytic practice though. We introduce a general unbiased and minimum variance
estimator upon the Beta-Binomially distributed Kemeny Hilbert space, which
allows for permutation ties to exist and be uniquely measured. As individual
permutation samples now exhibit unique random variance, a sample dependent
variance estimator must now be introduced into the linear model. We derive and
prove the Slutsky conditions to enable $t_{\nu}$-distributed Wald test
statistics to be constructed, while stably exhibiting Gauss-Markov properties
upon finite samples. Simulations demonstrate convergent decisions upon the two
orthonormal Slutsky corrected Wald test statistics, verifying the projective
geometric duality which exists upon the affine-linear Kemeny metric.
