Assess professionals' ability to identify and analyze common sources of measurement uncertainty and bias in environmental monitoring—instrumental (calibration, drift), sampling (design, representativeness), analytical and procedural errors, temporal/spatial variability, observer effects, and model assumptions; quantify uncertainty using techniques such as error propagation, Monte Carlo simulation, confidence intervals, and uncertainty budgets; evaluate bias impacts on decision-making; and propose practical strategies to reduce and transparently report uncertainty through QA/QC, calibration, optimized sampling design, sensor selection, data correction, and metadata documentation. Includes interpretation of case-based data and construction of simple uncertainty estimates.