In summary, the Bayesian inference-inspired strategy introduced in this paper for data evaluation represents a significant advance in understanding the contribution of different parameters to voltammetric data and their significance in experiment-to-experiment variability at a heterogeneous electrode in a manner that has not been possible in earlier studies. Perhaps remarkably, each data set in the pathologically variable [Fe(CN) 6 ] 3-/4-process conforms exceptionally well with simulated data derived from the Butler-Volmer model of electron transfer and mass transport by planar diffusion even though the variation in k 0 is quite substantial. Intriguingly, α, unlike k 0 , does not reveal significant experiment-to-experiment variation in this data analysis exercise. This may be expected if conformance to the Butler-Volmer model is strong.