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When reading peer-reviewed scientific literature describing any analysis ofempirical data, it is natural and correct to proceed with the underlyingassumption that experiments have made good faith efforts to ensure that theiranalyses yield unbiased results. However, particle physics experiments areexpensive and time consuming to carry out, thus if an analysis has inherentbias (even if unintentional), much money and effort can be wasted trying toreplicate or understand the results, particularly if the analysis isfundamental to our understanding of the universe.  In this note we discuss the significant biases that can result from databinning schemes. As we will show, if data are binned such that they provide thebest comparison to a particular (but incorrect) model, the resulting modelparameter estimates when fitting to the binned data can be significantlybiased, leading us to too often accept the model hypothesis when it is not infact true. When using binned likelihood or least squares methods there is ofcourse no a priori requirement that data bin sizes need to be constant, but weshow that fitting to data grouped into variable width bins is particularlyprone to produce biased results if the bin boundaries are chosen to optimizethe comparison of the binned data to a wrong model. The degree of bias that canbe achieved simply with variable binning can be surprisingly large.  Fitting the data with an unbinned likelihood method, when possible to do so,is the best way for researchers to show their analyses are not biased bybinning effects. Failing that, equal bin widths should be employed as across-check of the fitting analysis whenever possible.

Potential fitting biases resulting from grouping data into variable width bins