An effective, economical quality assurance program applied to lake monitoring uses a minimum number of quality assurance samples to provide maximum information about overall data quality. The resulting estimates of data quality can be expressed quantitatively as measures of precision, accuracy, representativeness, and comparability, The completeness of a data set can be assessed during the verification process and by an independent review (data audit). For a particular survey of lakes, the number and the types of quality assurance samples needed to evaluate measurement precision and accuracy can be calculated by identifying the components of potential variability in the data during preliminary field investigations (pilot studies) can be used to estimate the level of confidence associated with the routine sample measurements from the survey. The U.S. Environmental Protection Agency's National Surface Water Survey used laboratory performance evaluations, pilot studies, and measurements of quality assurance and quality control samples. This paper outlines the methods used for determining the confidence limits of data from the Eastern Lake Survey–Phase I and from the Phase II pilot study and spring sampling, including calculation of the mean and the variance of the observed measurement values that the analytical laboratories reported for audit samples. By assigning error limits to the confidence interval of the observed values, an equation was solved for the unknown number of replications (audit samples) needed to achieve the specified data quality objectives. Results of these calculations were used to determine the numbers of quality assurance samples needed for the Phase II summer and fall sampling. Recommendations for applying this approach to other monitoring projects include considerations of the size and complexity of the survey, the budgetary and logistical constraints, and the amount of information available regarding laboratory performance.