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Many cookstove studies conducted in the field fail to measure meaningful differences between different stove technologies. Although meaningful differences do not always exist, significant differences are often missed because of low statistical power. A numerical model has been developed to determine the minimum sample size necessary to ensure that cookstove field studies are well-designed, efficient, and have adequate statistical power to characterize the concentrations of pollutants inside homes. The numerical model uses a Monte Carlo prediction method to generate probabilistic distributions of indoor pollutant concentrations. The model is based on a series of user inputs, including emissions rate, home size, air-exchange rate, fuel-moisture content, and measurement error. Application of this model to an example situation showed that, even under optimistic measurement conditions, a substantially high number of test replicates would be required. This approach should allow organizations to select appropriate sample sizes to test cookstoves in the field and to identify factors that contribute to variability among tests.