Recall data of consumption of cigarettes, alcohol, fresh fruits and vegetables; visits to recreational sites, doctors’ offices, and local businesses; household expenditures; and individuals’ perceived probabilities of future events often contain reported numbers that appear to be rounded to nearby focal points (e.g., the closest 5 or 10). Failure to address this rounding has been show to produce biased estimates of marginal effects and willingness to pay. We investigate the relative performance of three count data models used with data of the kind typically found in recreation demand studies. We create a dataset based on observed recreational trip counts and associated trip costs that exhibits substantial rounding. We then conduct a Monte Carlo simulation exercise to compare estimated parameters, the average partial effect on an increase in trip cost, and average consumer surplus per trip for three alternative estimators: a standard Poisson model with no adjustment for rounding, a censored Poisson model, and the grouped Poisson model. The standard Poisson model with no adjustment for rounding exhibits significant, persistent bias, especially in estimates of average consumer surplus per trip. The grouped Poisson, in contrast, shows only slight biases and none at all in estimates of average consumer surplus per trip.