SIMBAD references

The main topics covered in this paper are (1) controlling significance levels when applying the same hypothesis test to many (possibly millions) of datasets; (2) dealing with the fact that for very large datasets hypotheses are rejected for trivially small departures from the null; (3) in the presence of noise, extreme values selected from samples for follow-up studies are often biased; (4) inference conducted on models fitted to data routinely underestimate the parameter standard errors if the selected model was informed by the observations; (5) obtaining overall least-mean-squared error estimates of a group of observations (e.g. a collection of star cluster masses); and (6) the effects of the violation of mathematical regularity conditions on model selection statistics.