  I have fit data from EPRB experiments of Weihs et al to a model that consists
of an EPRB thought experiment whose output is filtered. The filter implements
the assumptions made by most investigators: that EPRB experiments satisfy a
fair sampling assumption; and that detections and coincidences occur randomly
with Poisson distributions (this provides a basis for calculating standard
errors of coincidence counts, correlations, and so forth). The model does not
fit the data-predicted and observed counts of detections and coincidences
differ far too much, by a chi-square criterion. Logically, one must give up
fair sampling and/or Poisson errors and/or the assumption that the data derive
ultimately from an EPRB thought experiment. In the literature, giving up fair
sampling seems to be coupled to giving up EPRB, but to me it seems just as
sensible to keep EPRB and to give up fair sampling and Poisson errors. Some
rather ordinary mechanisms can violate fair sampling, and there is the
possibility in any experiment for uncontrolled and unmonitored factors to
contribute to unwanted variation. By sufficiently relaxing the fair sampling
and Poisson assumptions-it doesn't take much-my model can be made to fit the
data.
