**Abstract**

Quantitative data quality descriptors are important for evaluating and communicating acceptability of information used in environmental monitoring and assessment. In this chapter, we present (1) the rationale for establishing and using performance measures and MQOs in routine quality control planning and analysis, (2) field and laboratory methods for capturing input data required for performance calculations, and (3) approaches for setting data acceptability thresholds and determining the need for corrective actions. Relevant examples are available from local, regional, and national programs in the U.S. charged with monitoring and assessing aquatic biological condition, physical habitat, contaminants, and toxicity testing. We will describe techniques for calculating and determining acceptability of performance measures, such as, among other data quality indicators, precision, accuracy, sensitivity, representativeness, and completeness of field sampling, laboratory processing, and data management and analysis. Data types on which these examples will be based include benthic macroinvertebrates, fish assemblage, tissue body burden, laboratory analytical and toxicity testing, physical habitat, selected geomorphic characteristics, and algal toxins.

**Keywords:** precision, bias, indicators, error, corrective actions, acceptability
