Rich stimulus sampling for between-subjects designs improves model selection.

The choice of stimulus values to test in any experiment is a critical component of good experimental design. This study examines the consequences of random and systematic sampling of data values for the identification of functional relationships in experimental settings. Using Monte Carlo simulation, uniform random sampling was compared with systematic sampling of two, three, four, or N equally spaced values along a single stimulus dimension. Selection of the correct generating function (a logistic or a linear model) was improved with each increase in the number of levels sampled, with N equally spaced values and random stimulus sampling performing similarly. These improvements came at a small cost in the precision of the parameter estimates for the generating function.