In the past decade, the field of environmental risk assessment has seen vast improvements in the technologies and tools available to aid with quantification of human health and ecological risks. The development of publicly-available computational modelling tools, such as those based upon the risk based corrective action (RBCA) framework, have allowed those with a potentially less sophisticated understanding of the scientific rationale, equations and methodologies used to simulate environmental fate, toxicology and risk characterization processes to conduct and complete multimedia environmental risk assessments for regulatory approval. However, in many cases, this lack of understanding may result in the selection of inappropriate parameter data (i.e., chemical-, receptor-, or scenario-specific data), unknowingly increasing the uncertainty inherent within the overall assessment of potential risk. Accordingly, the use of inappropriate assumptions and parameters can result in an increase or decrease in the estimated risk, with significant implications on remediation costs or, more importantly, human or environmental health. Three case studies will be presented to illustrate the importance surrounding the selection of appropriate data input parameters, and the potential implications of this selection process on the results of an environmental risk assessment.