Uncertainty is inherent in all exposure and risk assessments in which mathematical models are used to extrapolate information beyond the domain of direct observation.
Uncertainty exists because models are mimics of reality.
In addition, the data available for inputs are seldom ever directly relevant to the defined assessment endpoint. The use of classical statistics to summarize the variability of direct observations is usually inappropriate for most exposure and risk projections. Classical statistics should be restricted to instances when data are obtained from either a random or stratified random design, appropriately averaged in space and time, and directly relevant to the target individuals or populations of interest. These situations are rare in human health and ecological risk assessments for which extrapolations are made in space and time and from non-human species to sensitive subgroups of the human population. Its practice can result in misleading statements of uncertainty and can misdirect the need for additional information.