  Comparing clustering of differently biased tracers of the dark matter
distribution offers the opportunity to reduce the cosmic variance error in the
measurement of certain cosmological parameters. We develop a formalism that
includes bias non-linearities and stochasticity. Our formalism is general
enough that can be used to optimise survey design and tracers selection and
optimally split (or combine) tracers to minimise the error on the
cosmologically interesting quantities. Our approach generalises the one
presented by McDonald & Seljak (2009) of circumventing sample variance in the
measurement of $f\equiv d \ln D/d\ln a$. We analyse how the bias, the noise,
the non-linearity and stochasticity affect the measurements of $Df$ and explore
in which signal-to-noise regime it is significantly advantageous to split a
galaxy sample in two differently-biased tracers. We use N-body simulations to
find realistic values for the parameters describing the bias properties of dark
matter haloes of different masses and their number density.
  We find that, even if dark matter haloes could be used as tracers and
selected in an idealised way, for realistic haloes, the sample variance limit
can be reduced only by up to a factor $\sigma_{2tr}/\sigma_{1tr}\simeq 0.6$.
This would still correspond to the gain from a three times larger survey volume
if the two tracers were not to be split. Before any practical application one
should bear in mind that these findings apply to dark matter haloes as tracers,
while realistic surveys would select galaxies: the galaxy-host halo relation is
likely to introduce extra stochasticity, which may reduce the gain further.
