Gender differences in decision making is a topic that has attracted much attention in the literature and the debate seems to be inconclusive. A method that is often used in the economics literature to account for gender effects is by estimating econometric structural models and testing the significance of the estimated parameters. In this paper we focus on estimations of preference models and we show how omitting to account for behavioural heterogeneity can lead to failures in identifying potential differences. Using data from risky choice experiments, we compare the traditional representative agent Maximum Likelihood Estimation approach against two more flexible inference methods that allow for heterogeneity at the individual level, the Maximum Simulated Likelihood Estimation, and the Hierarchical Bayesian modelling. We show how ignoring heterogeneity may lead to failures capturing gender differences and we suggest the use of Bayesian modelling to effectively estimate the underlying parameters.