Selection of likelihood parameters for complex models determines the effectiveness of Bayesian calibration

► Assumptions about unknown measurement variability affects posterior parameter distribution. ► This may lead to false parameter dependencies and uncertainty quantifications. ► Small data values are privileged by methods estimating missing standard variations and can lead to model underestimations. ► Inclusion of a model error term did improve model agreement and parameter posteriors.