Uncertainty

{
  "id": "MLC-T-ENV-028997",
  "term": "Uncertainty",
  "field": "Environment",
  "definition": "1. The term used in this guidance to describe lack of knowledge about models, parameters, constants, data, and beliefs. There are many sources of uncertainty, including the science underlying a model, uncertainty in model parameters and input data, observation error, and code uncertainty. Additional study and collecting more information allows error that stems from uncertainty to be minimized/reduced (or eliminated). In contrast, variability (see definition) is irreducible but can be better characterized or represented with further study.\n\n2. Imperfect knowledge concerning the present or future state of the system under consideration; a component of risk resulting from imperfect knowledge of the degree of hazard or of its spatial and temporal distribution.\n\n3. Uncertainty occurs because of a lack of knowledge. It is not the same as variability. For example, a risk assessor may be very certain that different people drink different amounts of water but may be uncertain about how much variability there is in water intakes within the population. Uncertainty can often be reduced by collecting more and better data, whereas variability is an inherent property of the population being evaluated. Variability can be better characterized with more data but it cannot be reduced or eliminated. Efforts to clearly distinguish between variability and uncertainty are important for both risk assessment and risk characterization.\n\n4. A limit to knowledge where it is impossible to describe an existing state or future outcome exactly. Uncertainty has three primary components: 1) variability (also called \"heterogeneity\" or \"stochasticity\"), a component of all biological systems, which represents actual differences in the value of a parameter or attribute among units in a (statistical) population; 2) ignorance, which represents a lack of knowledge about the true value of a parameter that can result from inadequate or imperfect measurement; and 3) error, which results from the use of the wrong methods, models or data in analysis activities. (derived from: Munns 2002)\n\n5. Uncertainty refers to our inability to know for sure - it is often due to incomplete data. For example, when assessing the potential for risks to people, toxicology studies generally involve dosing of sexually mature test animals such as rats as a surrogate for humans. Since we don't really know how differently humans and rats respond, EPA often employs the use of an uncertainty factor to account for possible differences. Additional consideration may also be made if there is some reason to believe that the very young are more susceptible than adults, or if key toxicology studies are not available.\n\n6. A lack of knowledge about certain factors in a study which can reduce the confidence in conclusions drawn from data in that study; it is opposed to variability which is a result of true variation in characteristics of the environment.\n\n7. A measure of imprecision, bias, or other sources of variability in a given value.\n\n8. Lack of knowledge concerning an event, state, model, or parameter. Uncertainty, unlike variability, may be reduced by research or observation.\n\n9. Uncertainty represents a lack of knowledge about factors affecting exposure or risk and can lead to inaccurate or biased estimates of exposure. The types of uncertainty include: scenario, parameter, and model.\n\n10. The estimated bounds of the deviation from the mean value, expressed generally as a percentage of the mean value. Taken ordinarily as the sum of (1) the random errors (errors of precision) at the 95% confidence level, and (2) the estimated upper bound of the systematic error (errors of accuracy).\n\n11. Lack of sureness or confidence in predictions of models or results of measurements often due to stochastic variation, or lack of knowledge based on an incomplete characterization, understanding, or measurement of a system. Identifying uncertainties helps define the limitations of a scientific study.\n\n12. The unknown effects of parameters, variables, or relationships that cannot or have not been verified or estimated by measurement or experimentation.\n\n13. Uncertainty represents a lack of knowledge about factors affecting exposure/toxicity assessments and risk characterization and can lead to inaccurate or biased estimates of risk and hazard. Some of the types of uncertainty include scenario uncertainty, parameter uncertainty, and model uncertainty.\n\n14. An expression of the lack of knowledge, usually given as a range or group of plausible alternatives.\n\n15. A term for the fuzzy concept of qualifying statement of what is known or concluded with quantitative statements of probability. Uncertainty usually has two aspects: (1) that created by the experimental (i.e., observational) error associated with taking observations, and (2) that implied by the use of imperfect models.\n\n16. The range of values that contains the true value of what is being evaluated at some level of confidence. {OW/EAD} {OPP} {OAR/OAQPS} {OW/TSC}\n\n17. A measure of the total variability associated with sampling and measuring that includes the two major error components: systematic error (bias) and random error. {ORD} {OECA} {ORCR}\n\n18. Describes lack of knowledge about models, parameters, constants, data, and beliefs. There are many sources of uncertainty, including the science underlying a model, uncertainty in model parameters and input data, observation error, and code uncertainty. Additional study and collecting more information allows error that stems from uncertainty to be minimized/reduced (or eliminated). In contrast, variability (see definition) is irreducible but can be better characterized or represented with further study.\n\n19. Lack of knowledge about specific variables, parameters, models, or other factors. Examples include limited data regarding the concentration of a contaminant in an environmental medium and lack of information on local fish consumption practices. Uncertainty may be reduced through further study.\n\n20. A measure of the inherent variability of repeated observation measurements of a quantity including quantities evaluated by statistical methods and by other means.\n\n21. The term used in this document to describe lack of knowledge about models, parameters, constants, data, and beliefs. There are many sources of uncertainty, including the science underlying a model, uncertainty in model parameters and input data, observation error, and code uncertainty. Additional study and collecting more information allows error that stems from uncertainty to be minimized/reduced (or eliminated). In contrast, variability (see definition) is irreducible but can be better characterized or represented with further study (EPA 2002b, Shelly et al. 2000).\n\n22. Uncertainty with respect to NIST-traceability. See the definition of NIST-traceable in this section.\n\n23. Uncertainty represents a lack of knowledge about factors affecting exposure/toxicity assessments and risk characterization and can lead to inaccurate or biased estimates of risk and hazard. Some of the types of uncertainty include scenario uncertainty, parameter uncertainty, and model uncertainty. [EPA 1997a, EPA 2004]\n\n24. Lack of knowledge regarding the true value of a quantity, such as a specific characteristic (e.g., mean, variance) of a distribution for variability, or regarding the appropriate and adequate inference options to use to structure a model or scenario. These are also referred to as model uncertainty and scenario uncertainty. Lack of knowledge uncertainty can be reduced by obtaining more information through research and data collection, such as through research on mechanisms, larger sample sizes or more representative samples. [FAO/WHO 2003b]\n\n25. Uncertainty occurs because of a lack of knowledge. It is not the same as variability. For example, a risk assessor may be very certain that different people drink different amounts of water but may be uncertain about how much variability there is in water intakes within the population. Uncertainty can often be reduced by collecting more and better data, whereas variability is an inherent property of the population being evaluated. Variability can be better characterized with more data but it cannot be reduced or eliminated. Efforts to clearly distinguish between variability and uncertainty are important for both risk assessment and risk characterization. [EPA 2003]\n\n26. The range of values that contains the true value of what is being evaluated at some level of confidence.\n\n27. A measure of the total variability associated with sampling and measuring that includes the two major error components: systematic error (bias) and random error.\n\n28. Imperfect knowledge or lack of precise knowledge of the real world either for specific values of interest or in the description of the system.\n\n29. Uncertainty is imperfect knowledge of the microbiological hazard (e.g., its virulence), environmental pathways/processes, or the human populations under consideration (from MRA). Uncertainty represents a lack of knowledge about factors affecting risk assessments and can lead to inaccurate or biased estimates or risk and hazard. Some of the types of uncertainty include scenario uncertainty, parameter uncertainty, and model uncertainty. Uncertainty can be reduced by further study. NRC Definition: Lack or incompleteness of information. Quantitative uncertainty analysis attempts to analyze and describe the degree to which a calculated value may differ from the true value; it sometimes uses probability distributions. Uncertainty depends on the quality, quantity, and relevance of data and on the reliability and relevance of models and assumptions. [USDA/FSIS/2012-001; EPA/100/J12/001]\n\n30. Uncertainty is a prominent feature of the benefits and costs of climate change. Decision makers need to compare risk of premature or unnecessary actions with risk of failing to take actions that subsequently prove to be warranted. This is complicated by potential irreversibilities in climate impacts and long term investments.\n\n31. Uncertainty is imperfect knowledge of the microbiological hazard (e.g., its virulence), environmental pathways/processes, or the human populations under consideration (from MRA). Uncertainty represents a lack of knowledge about factors affecting risk assessments and can lead to inaccurate or biased estimates or risk and hazard. Some of the types of uncertainty include scenario uncertainty, parameter uncertainty, and model uncertainty. Uncertainty can be reduced by further study. NRC Definition: Lack or incompleteness of information. Quantitative uncertainty analysis attempts to analyze and describe the degree to which a calculated value may differ from the true value; it sometimes uses probability distributions. Uncertainty depends on the quality, quantity, and relevance of data and on the reliability and relevance of models and assumptions.\n\n32. A lack of data or an incomplete understanding of the context of the risk assessment decision. It can be either qualitative or quantitative. [U.S. EPA Exposure Factors Handbook: 2011 Edition (U.S. EPA Exposure Factors Handbook: 2011 Edition (https://cfpub.epa.gov/ncea/risk/recordisplay.cfm?deid=236252))",
  "subject": "Science and research",
  "references": [
    "Modeling Glossary",
    "Waste and Cleanup Risk Assessment Glossary",
    "Integrated Risk Information System (IRIS) Glossary; EPA 2003",
    "Core Ecosystem Services Research Program Standard Lexicon",
    "Risk Assessment Glossary",
    "Ecological Risk Assessment Glossary of Terms",
    "Great Lakes Quality Mgmt Plan Glossary",
    "CADDIS Glossary",
    "Exposure Factors Handbook: Glossary",
    "Radon Glossary of Terms",
    "Radiation Protection Radiation Glossary",
    "Benchmark Dose Software (BMDS) Glossary of Terms",
    "EPA 1997a, EPA 2004",
    "FDA 2002",
    "NIST/SEMATECH 2005b",
    "Forum on Environmental Measurements (FEM) Glossary",
    "EPA Definitions of Terms Relevant to PRA and References for Further Reading at http://www.epa.gov/oswer/riskassessment/rags3adt/pdf/appendixe.pdf",
    "National Cancer Institute Thesaurus",
    "EPA Guidance on the Development, Evaluation and Application of Environmental Models EPA/100/K-09/003 | March 2009 at http://www.epa.gov/crem/library/cred_guidance_0309.pdf",
    "40 CFR 1065.1001 (CFR 2013)",
    "Thesaurus of Terms Used in Microbial Risk Assessment",
    "40 CFR 702.33 (CFR 2019)",
    "Environmental Sampling and Analytical Methods (ESAM) Program Glossary",
    "NOAA Climate Program Office Glossary",
    "USDA/FSIS/2012-001; EPA/100/J12/001",
    "EPA ExpoBox Terminology"
  ],
  "url": "https://mlchart.com/terminology/environment/uncertainty/"
}