There is currently no consistent approach to modelling galaxy bias evolution in cosmological inference .
This lack of a common standard makes the rigorous comparison or combination of probes difficult .
We show that the choice of biasing model has a significant impact on cosmological parameter constraints for a survey such as the Dark Energy Survey ( DES ) , considering the 2-point correlations of galaxies in five tomographic redshift bins .
We find that modelling galaxy bias with a free biasing parameter per redshift bin gives a Figure of Merit ( FoM ) for Dark Energy equation of state parameters w _ { 0 } ,w _ { a } smaller by a factor of 10 than if a constant bias is assumed .
An incorrect bias model will also cause a shift in measured values of cosmological parameters .
Motivated by these points and focusing on the redshift evolution of linear bias , we propose the use of a generalised galaxy bias which encompasses a range of bias models from theory , observations and simulations , b ( z ) = c + ( b _ { 0 } - c ) / D ( z ) ^ { \alpha } , where parameters c,b _ { 0 } and \alpha depend on galaxy properties such as halo mass .
For a DES-like galaxy survey we find that this model gives an unbiased estimate of w _ { 0 } ,w _ { a } with the same number or fewer nuisance parameters and a higher FoM than a simple b ( z ) model allowed to vary in z-bins .
We show how the parameters of this model are correlated with cosmological parameters .
We fit a range of bias models to two recent datasets , and conclude that this generalised parameterisation is a sensible benchmark expression of galaxy bias on large scales .