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We use the Gigaparsec WiggleZ (GiggleZ) simulations to characterise galaxybias and its scale dependence for a range of redshifts and halo masses in astandard $\Lambda$LCDM cosmology. Assuming bias converges to a scaleindependent form at large scales, we develop a phenomenological model whichfully expresses the mass and redshift dependence of bias and its scaledependence in real or redshift space. We then use this to illustrate howscale-dependent bias can systematically skew measurements of the growth-rate ofcosmic structure obtained from redshift-space distortion measurements. Whendata is fit only to scales $k_{\rm max}{\le}0.1$ $[h^{-1} \rm{Mpc}]^{-1}$, wefind that these effects are significant only for large biases ($b{\gtrsim}3$)at large redshifts ($z{\gtrsim}1$). However, when smaller scales areincorporated ($k_{\rm max}{\le}0.2$ $[h^{-1} \rm{Mpc}]^{-1}$) to increasemeasurement precision, the combination of reduced statistical uncertainty andincreased scale dependent bias can result in highly significant systematics formost large halos across all redshifts. We identify several new interestingaspects of bias, including a significant large-scale bias boost for small halosat low-redshifts due to substructure effects ($\sim$20\% for Milky Way-likesystems) and a nearly redshift-independent halo mass (corresponding to aredshift-space bias of ${\sim}1.5$) for which halo bias has little-or-no scaledependence on scales greater than $3$ $[h^{-1} {\rm Mpc}]$. This suggests anoptimal strategy of targeting bias ${\sim}{1.5}$ systems for clustering studieswhich are dominated by systematic uncertainties in how observed halo (orgalaxy) distributions map to their underlying mass distribution, such ascosmological measurements of neutrino masses. Code for generating our fittingformula is publicly available at http://gbpoole.github.io/Poole_2014a_code/(Abridged)

The Gigaparsec WiggleZ Simulations: Characterising scale dependant bias and associated systematics in growth of structure measurements