This study analyzes the cellular microelectrode voltage measurement errors produced by active and passive current regulation, and the propagation of these errors into cellular barrier function parameter estimates. The propagation of random and systematic errors into these parameters is accounted for within a Riemannian manifold framework consistent with information geometry. As a result, the full non-linearity of the model parameter state dependence, the instrumental noise distribution, and the systematic errors associated with the voltage to impedance conversion, are accounted for. Specifically, cellular model parameters are treated as the coordinates of a model space manifold that inherits a Riemannian metric from the data space. The model space metric is defined in terms of the pull back of an instrumental noise-dependent Fisher information metric. Additional noise sources produced by the evaluation of the cell-covered electrode model that is a function of a naked electrode random variable are also included in the analysis. Based on a circular cellular micro-impedance model in widespread use, this study shows that cellular barrier function parameter estimates are highly model state dependent. Systematic errors produced by coaxial lead capacitances and circuit loading can also lead to significant and model state-dependent parameter errors and should, therefore, be either reduced or corrected for analytically.