Experimental data are rarely, if ever, distributed as a normal (Gaussian) distribution, in real world applications. A large set of data—such as the cross sections for particle scattering as a function of energy contained in the archives of the Particle Data Group 1 —is a compendium of all published data, and hence, unscreened. For many reasons, these data sets have many outliers—points well beyond what is expected from a normal distribution—thus ruling out the use of conventional χ 2 techniques. We suggest an adaptive algorithm that applies to the data sample a sieve whose mesh is coarse enough to let the background fall through, but fine enough to retain the preponderance of the signal, thus sifting the data. The “Sieve ” algorithm gives a robust estimate of the best-fit model parameters in the presence of a noisy background, together with a robust estimate of the model parameter errors, as well as a determination of the goodness-of-fit of the data to the theoretical hypothesis. Computer simulations were carried out to test the algorithm for both its accuracy and stability under varying background conditions. 1
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