Simulating the electrical behavior of the heart requires accounting for parameter errors, model inaccuracies, and individual variations in settings, which can all be influenced by user choices or disease conditions. Building on previous findings, we employ bi-ventricular activation simulations and robust uncertainty quantification (UQ) techniques based on polynomial chaos expansion (PCE) to map variability in propagation simulations as a result of parameter uncertainty. The PCE approach offers efficient stochastic exploration with reduced computational demands. To ensure reliable results, we focused here on the importance of testing for polynomial order and sample size, aiming to obtain accurate outcomes with minimal computational burden. Order testing involves determining the polynomial degree used for calculating statistics, whereas sample testing pertains to identifying the necessary number and values of the samples, both aimed at ensuring consistency in the results. Through a thorough investigation employing bi-ventricular activation simulations and UncertainSCI, we quantified the effects of physiological variability in conduction velocity on cardiac activation simulations and the influence of polynomial order and sampling on uncertainty computations. Our results show that the selection of the appropriate polynomial degree order and sample dataset influences the outcomes of simulations and should be a required step before performing a UQ analysis.