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Recently, we have introduced the use of techniques drawn from Bayesianstatistics to recover kinetic and thermodynamic parameters from voltammetricdata, and were able to show that the technique of large amplitude acvoltammetry yielded significantly more accurate parameter values than theequivalent dc approach. In this paper we build on this work to show that thisapproach allows us, for the first time, to separate the effects of randomexperimental noise and inherent system variability in voltammetric experiments.We analyse ten repeated experimental data sets for the $[$Fe(CN)$_6]^{3-/ 4-}$process, again using large-amplitude ac cyclic voltammetry. In each of the tencases we are able to obtain an extremely good fit to the experimental data andobtain very narrow distributions of the recovered parameters governing both thefaradaic (the reversible formal faradaic potential, $E_0$, the standardheterogeneous charge transfer rate constant $k_0$, and the charge transfercoefficient $\alpha$) and non-faradaic terms (uncompensated resistance, $R_u$,and double layer capacitance, $C_{dl}$). We then employ hierarchical Bayesianmethods to recover the underlying "hyperdistribution" of the faradaic andnon-faradaic parameters, showing that in general the variation between theexperimental data sets is significantly greater than suggested by individualexperiments, except for $\alpha$ where the inter-experiment variation wasrelatively minor. Correlations between pairs of parameters are provided, andfor example, reveal a weak link between $k_0$ and $C_{dl}$ (surface activity ofa glassy carbon electrode surface). Finally, we discuss the implications of ourfindings for voltammetric experiments more generally.

Separating the effects of experimental noise from inherent system variability in voltammetry: the $[$Fe(CN)$_6]^{3-/ 4-}$ process