An in-depth understanding of fundamental heterogeneous catalysis can be obtained by the use of Microkinetic Analysis of the elementary reaction steps. Unfortunately, for complex reaction networks, the sheer number of parameters present in such models can make the interpretation and deeper understanding of such microkinetic models very difficult. In this work, a Microkinetic Analysis Methodology, which incorporates statistical methods, is demonstrated, with application to a typical set of fundamental catalytic reactions. A uniform statistical experimental design is implemented, varying a selection of parameters within the microkinetic model. The responses of the simulated model are fitted to construct approximation models. We illustrate the extension of some known methods in order to successfully implement the proposed methodology, such as Kriging approximation models applied to additive log-ratio transformations of the surface coverage compositions, as well as a multivariate scaled EIGF criterion to iteratively improve the global model fit. We demonstrate that the methodology yields statistical approximation models that are capable of yielding accurate predictions of the complete parameter space. The proposed methodology can be applied to any number of microkinetic model parameters of interest.