This work focuses on the development of a dedicated experimental methodology that allows for a better stochastic characterization of catalytic recombination parameters for reusable ceramic matrix composite materials when dealing with uncertain measurements and model parameters. As one of the critical factors affecting the performance of such materials, the contribution to the heat flux of the exothermic recombination reactions at the vehicle surface must be carefully assessed. In this work, we first use synthetic data to test whether or not the proposed experimental methodology brings any advantages in terms of uncertainty reduction on the sought out parameters compared to more traditional experimental approaches in the literature. The evaluation is done through the use of a Bayesian framework developed in a previous work with the advantage of being able to fully and objectively characterize the uncertainty on the calibrated parameters. The synthetic dataset is adapted for testing ceramic matrix composites by carefully choosing adequate auxiliary materials whose heat flux measurements have the capability of reducing the resulting uncertainty on the catalytic parameter of the thermal protection material itself when tested under the same flow conditions. We then propose a comprehensive set of real wind tunnel testing cases for which stochastic analyses are carried out. The physical model used for the estimations consists of a 1D boundary layer solver along the stagnation line in which the chemical production term included in the surface mass balance depends on the catalytic recombination efficiency. All catalytic parameters of the auxiliary and thermal protection materials are calibrated jointly with the boundary conditions of the experiments. The testing methodology confirms to be a reliable experimental approach for characterizing these materials while reducing the uncertainty on the calibrated catalytic efficiencies by more than 50 %. An account of the posteriors summary statistics is provided to enrich the current state-of-the-art experimental databases.