  This letter considers stochastic geometry modelling (SGM) for estimating the
signal-to-interference-and-noise ratio (SINR) and throughput of CSMA networks.
We show that, despite its compact mathematical formulation, SGM has serious
limitations in terms of both accuracy and computational efficiency. SGM often
severely underestimates the SINR versus ns-3 simulations, yet as it neglects
the sensing overhead when mapping SINR to throughput, SGM usually overestimates
the throughput substantially. We propose our hybrid model for CSMA, which we
argue is a superior modelling approach due to being significantly more accurate
and at least one order of magnitude faster to compute than SGM.
