Data prefiltering is often used in linear system identification to increase model accuracy in a specified frequency band, as prefiltering is equivalent to a frequency weighting on the prediction error function. However, this interpretation applies only to a strictly linear setting of the identification problem. In this note, the role of data and error prefiltering in nonlinear system identification is analyzed and a frequency domain interpretation is provided, based on the Volterra series representation of nonlinear systems. Simulation results illustrate the conclusions of the analysis.