As epidemiologists search for smaller and smaller effects, the statist
ical uncertainty in their studies can be dwarfed by biases and systema
tic uncertainty. We here suggest that Monte Carlo techniques are very
useful to estimate some of these biases and uncertainties, and perhaps
to avoid them entirely. We illustrate this by two simple Monte Carlo
simulations. First, we show how often false positive findings, and som
etimes false negative findings, can result from 33 differential miscla
ssification of the exposure status. Secondly, we show how a bias, that
we call ''the binning bias,'' can be caused if the investigator choos
es bin boundaries after he has seen the data. We show how an allowance
might be made for such a bias by increasing the uncertainty bounds. T
his would put the presentation of the results on a par with the presen
tation in physical sciences where a quantitative estimate of systemati
c errors is routinely included with the final result. Finally, we sugg
est how similar Monte Carlo simulations carried out before and during
the study can be used to avoid the biases entirely.