  Different ways of extracting parameters of interest from combined data sets
of separate experiments are investigated accounting for the systematic errors.
It is shown, that the frequentist approach may yield larger $\chi^2$ values
when compared to the Bayesian approach, where the systematic errors have a
Gaussian distributed prior calculated in quadrature. The former leads to a
better estimation of the parameters. A maximum-likelihood method, applied to
different "gedanken" and real LHC data, is presented. The results allow to
choose an optimal approach for obtaining the fit based model parameters.
