Monte Carlo-generalised Polynomial Chaos (MC-gPC) has already been thoroughly studied in the literature [1,2,3,4,5,6,7]. MC-gPC both builds a gPC based reduced model of a partial differential equation (PDE) of interest and solves it with an intrusive MC scheme in order to propagate uncertainties. This reduced model captures the behaviour of the solution of a set of PDEs subject to some uncertain parameters modeled by random variables. MC-gPC is an intrusive method, it needs modifications of a code in order to be applied. This may be considered a drawback. But, on another hand, important computational gains obtained with MC-gPC have been observed on many (linear [1,3] or nonlinear [4,5,6,7]) applications. The MC-gPC resolution of Boltzmann equation has been investigated in many different ways: the wellposedness of the gPC based reduced model has been studied in [2,6], the convergence with respect to the truncation order P has been theoretically and numerically studied [2, 1], the coupling to nonlinear physics has been performed in [7,6]. But the study of the MC noise remains, to our knowledge, to be done. This is the purpose of this paper.