  Control of systematic uncertainties in the use of Type Ia supernovae as
standardized distance indicators can be achieved through contrasting subsets of
observationally-characterized, like supernovae. Essentially, like supernovae at
different redshifts reveal the cosmology, and differing supernovae at the same
redshift reveal systematics, including evolution not already corrected for by
the standardization. Here we examine the strategy for use of empirically
defined subsets to minimize the cosmological parameter risk, the quadratic sum
of the parameter uncertainty and systematic bias. We investigate the optimal
recognition of subsets within the sample and discuss some issues of
observational requirements on accurately measuring subset properties.
Neglecting like vs. like comparison (i.e. creating only a single Hubble
diagram) can cause cosmological constraints on dark energy to be biased by
1\sigma or degraded by a factor 1.6 for a total drift of 0.02 mag. Recognition
of subsets at the 0.016 mag level (relative differences) erases bias and
reduces the degradation to 2%.
