  Binned maximum likelihood fits are an attractive option when analysing large
datasets, but require care when computing likelihoods of continuous PDFs in
bins. For many years the widely used statistical modelling package RooFit
evaluated probabilities at the bin centre, leading to significant biases for
strongly curved probability density functions. We demonstrate the biases with
real-world examples, and introduce a PDF class to RooFit that removes these
biases. The physics and computation performance of this new class are
discussed.
