We present an extended methodology for parametric inference in complex population balance models. The aim is twofold. Firstly, it is assumed that the parameter distribution of the model is a multimodal Gaussian rather than a unimodal Gaussian. After projection of experimental data through a response surface approximation, estimates for the parameters and their uncertainties along with the associated weights of each mode are established. Secondly, the methodology is used to ask the following question - if n professors each have a 'best' estimate of a particular parameter, which of these estimates is more likely to be correct? A toy example is used to show the applicability of the methodology, aiding in the discrimination between a bimodal and trimodal parameter distribution. The identification of the 'best' model parameter among two conflicting estimates is demonstrated in an example from granulation modelling.