  We present a new fitting technique based on the parametric bootstrap method,
which relies on the idea to produce artificial measurements using the estimated
probability distribution of the experimental data. In order to investigate the
main properties of this technique, we develop a toy model and we analyze
several fitting conditions with a comparison of our results to the ones
obtained using both the standard $\chi^2$ minimization procedure and a Bayesian
approach. Furthermore, we investigate the effect of the data systematic
uncertainties both on the probability distribution of the fit parameters and on
the shape of the expected goodness-of-fit distribution. Our conclusion is that,
when systematic uncertainties are included in the analysis, only the bootstrap
procedure is able to provide reliable confidence intervals and p-values, thus
improving the results given by the standard $\chi^2$ minimization approach. Our
technique is then applied to an actual physics process, the real Compton
scattering off the proton, thus confirming both the portability and the
validity of the bootstrap-based fit method.
