Robust scientific inference is crucial to ensure evidence-based decision making. Accordingly, the selection of appropriate statistical tools and experimental designs is integral to achieve accuracy from data analytical processes. Environmental monitoring of water quality has become increasingly common and widespread as a result of technological advances, leading to an abundance of datasets. We conducted a scoping review of the water quality literature and found that correlation and linear regression are by far the most used statistical tools. However, the accuracy of inferences drawn from ordinary least squares (OLS) techniques depends on a set of assumptions, most prominently: (a) independence among observations, (b) normally distributed errors, (c) equal variances of errors, and (d) balanced designs. Environmental data, however, are often faced with temporal and spatial dependencies, and unbalanced designs, thus making OLS techniques not suitable to provide valid statistical inferences. Generalized least squares (GLS), linear mixed-effect models (LMMs), and generalized linear mixed-effect models (GLMMs), as well as Bayesian data analyses, have been developed to better tackle these problems. Recent progress in the development of statistical software has made these approaches more accessible and user-friendly. We provide a high-level summary and practical guidance for those statistical techniques.

The primary goals of environmental monitoring are to indicate whether unexpected changes related to development are occurring in the physical, chemical, and biological attributes of ecosystems and to inform meaningful management intervention. Although achieving these objectives is conceptually simple, varying scientific and social challenges often result in their breakdown. Conceptualizing, designing, and operating programs that better delineate monitoring, management, and risk assessment processes supported by hypothesis-driven approaches, strong inference, and adverse outcome pathways can overcome many of the challenges. Generally, a robust monitoring program is characterized by hypothesis-driven questions associated with potential adverse outcomes and feedback loops informed by data. Specifically, key and basic features are predictions of future observations (triggers) and mechanisms to respond to success or failure of those predictions (tiers). The adaptive processes accelerate or decelerate the effort to highlight and overcome ignorance while preventing the potentially unnecessary escalation of unguided monitoring and management. The deployment of the mutually reinforcing components can allow for more meaningful and actionable monitoring programs that better associate activities with consequences. Integr Environ Assess Manag 2017;13:877–891. © 2017 The Authors. Integrated Environmental Assessment and Management Published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC)