  In the real world, experimental data are rarely, if ever, distributed as a
normal (Gaussian) distribution. As an example, 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--is a compendium of all published data, and
hence, unscreened. Inspection of similar data sets quickly shows that, 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
$\chi^2$ techniques. This note suggests an adaptive algorithm that allows a
phenomenologist to apply 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. A prescription is given for finding 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. Extensive computer simulations are carried out to test the
algorithm for both its accuracy and stability under varying background
conditions.
