Effective management of harvested wildlife often requires accurate estimates of the number of animals harvested annually by hunters. A variety of techniques exist to obtain harvest data, such as hunter surveys, check stations, mandatory reporting requirements, and voluntary reporting of harvest. Agencies responsible for managing harvested wildlife such as deer (Odocoileus spp.), elk (Cervus elaphus), and pronghorn (Antilocapra americana) are challenged with balancing the cost of data collection versus the value of the information obtained. We compared precision, bias, and relative cost of several common strategies, including hunter self-reporting and random sampling, for estimating hunter harvest using a realistic set of simulations. Self-reporting with a follow-up survey of hunters who did not report produces the best estimate of harvest in terms of precision and bias, but it is also, by far, the most expensive technique. Self-reporting with no follow-up survey risks very large bias in harvest estimates, and the cost increases with increased response rate. Probability-based sampling provides a substantial cost savings, though accuracy can be affected by nonresponse bias. We recommend stratified random sampling with a calibration estimator used to reweight the sample based on the proportions of hunters responding in each covariate category as the best option for balancing cost and accuracy. © 2011 The Wildlife Society.