The worth of additional data to a digital model of the Tucson basin, Arizona was computed using a basic form of statistical decision theory (Bayesian). The model variables for which additional data were hypothesized included the aquifer coefficients of storage and transmissivity, initial water levels, discharge, and recharge. The worth of data was evaluated in terms of the expected reduction in error in predicted water levels associated with collection of more data on one variable at one point in the model. A limited number of tests suggested that the Tucson basin model could be improved most by obtaining more data on discharge and recharge in areas where these variables were large, and by obtaining more data on transmissivity where it was uncertain.