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

Statistical and systematical errors in analyses of separate experimental data sets in high energy physics