  Tests of cosmological models via measurements of galaxy correlations will
require increasing modeling accuracy, given the high precision of measurements
promised by forthcoming galaxy surveys. In this work we investigate the biases
introduced in parameter estimation when using different approximations in the
modeling of the galaxy two point correlation function. We study this for two
example surveys, with different binning strategies, for measurements of the
Primordial non-Gaussianity parameter $f_{\mathrm{NL}}$ and the growth rate of
structures $\gamma$. We then investigate the same issue for the nDGP model, to
see if results will change for a different cosmological model. Our results show
that failing to properly account for radial and angular separation between
galaxies will induce a considerable shift in parameters best fit estimates, the
bias being larger for thicker redshift bins. When accounting for radial
evolution within the bins by integrating over z, such shifts are reduced but
still present. We then introduce a new hybrid model, which we call 3D radial,
where we neglect the purely wide angle terms, but include a proper 3D modeling
of the system by including radial modes. Using this model, we show that biases
are greatly reduced, making it an accurate formalism to be safely used for
forthcoming galaxy surveys. This moreover confirms other recent findings on the
importance of including radial modes to accurately model the galaxy correlation
function.
