Correcting for sampling bias in quantitative measures of selection when fitness is discrete

We show with a simulation that nonrepresentative sampling of two discrete f itness classes leads to biased estimates of selection. Systematic underesti mation occurs if the selected class is overrepresented in the sample and ov erestimation if the unselected class is overrepresented. The bias is greate r the stronger the selection intensity, the smaller the true fraction of in dividuals favored by selected, and the lower the sample size. We present a simple method that allows a posteriori statistical correction in cases of b iased sampling given a separate estimate of the actual class representation , describe its practical implementation, and show that it works.