Understanding nonlinear effects is crucial for accurate risk assessment and regulatory decision-making. Nonlinear relationships can lead to underestimation or overestimation of risk if not properly accounted for. For example, a threshold model suggests that there is a dose below which no effect is observed, which is critical for determining safe exposure levels.
