Risk and uncertainty

Objective: Explain risk assessment and uncertainty range, distinguish their meanings, and apply them to a described case.

Risk assessment: Risk assessment evaluates hazards, exposure, likelihood, and consequences using stated assumptions. Uncertainty range: An uncertainty range communicates plausible estimates given data and model limitations. Risk estimates are conditional on evidence and assumptions. Sensitivity analysis identifies influential inputs rather than proving that a model is correct. Precaution can support proportionate action under uncertainty, while additional research improves the basis for decisions. Public perceptions reflect experience, trust, and distribution of harm as well as statistical probability. Reports should distinguish an uncertain estimate from a demonstrated absence of harm and identify who bears potential consequences. Application: A study estimates the potential effects of a contaminant pathway. This most directly illustrates risk assessment. A report gives a range rather than an unsupported exact value. This most directly illustrates uncertainty range.

Objective: Explain sensitivity analysis and precaution, distinguish their meanings, and apply them to a described case.

Sensitivity analysis: Sensitivity analysis tests how results change when assumptions or inputs change. Precaution: A precautionary approach considers protective action when potential harms and incomplete knowledge warrant it. Risk estimates are conditional on evidence and assumptions. Sensitivity analysis identifies influential inputs rather than proving that a model is correct. Precaution can support proportionate action under uncertainty, while additional research improves the basis for decisions. Public perceptions reflect experience, trust, and distribution of harm as well as statistical probability. Reports should distinguish an uncertain estimate from a demonstrated absence of harm and identify who bears potential consequences. Application: Analysts vary a removal-rate assumption to assess its influence. This most directly illustrates sensitivity analysis. A decision limits exposure while better evidence is collected. This most directly illustrates precaution.

Objective: Explain risk perception and distribution of risk, distinguish their meanings, and apply them to a described case.

Risk perception: Risk perception is how people interpret hazards and may differ from measured probability estimates. Distribution of risk: Risk distribution describes who experiences burdens and whether averages hide unequal exposure. Risk estimates are conditional on evidence and assumptions. Sensitivity analysis identifies influential inputs rather than proving that a model is correct. Precaution can support proportionate action under uncertainty, while additional research improves the basis for decisions. Public perceptions reflect experience, trust, and distribution of harm as well as statistical probability. Reports should distinguish an uncertain estimate from a demonstrated absence of harm and identify who bears potential consequences. Application: Residents weigh familiarity and personal experience when judging a hazard. This most directly illustrates risk perception. An assessment maps exposure across neighborhoods. This most directly illustrates distribution of risk.