A Bayesian method for computing sample size and cost requirements for stratified random sampling of pond water

Estimating average environmental pollution concentrations and its variance is a fairly straight forward task in stratified random sampling. A more challenging concept is the introduction of the cost factor into this environmental model. Traditional statistical techniques have incorporated costs from sampling within a stratum as well as stratum weights to determine the stratum size and overall required sample size. Information in the form of informative prior distributions to determine a more coherent variance in the system yield a more precise Bayesian approach to the sample size and cost calculations. This approach results in a more efficient sampling strategy in terms of cost when considering a pre-specified margin of error for the sampling mean as well as the more complicated situation of correlation among the stratum samples.