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Risk is the lack of certainty about the outcome of making a particular choice. Statistically, the level of downside risk can be calculated as the product of the probability that harm occurs (e.g., that an accident happens) multiplied by the severity of that harm (i.e., the average amount of harm or more conservatively the maximum credible amount of harm).
ERM can also be described as a risk-based approach to managing an enterprise, integrating concepts of internal control, the Sarbanes–Oxley Act, data protection and strategic planning. ERM is evolving to address the needs of various stakeholders, who want to understand the broad spectrum of risks facing complex organizations to ensure they are ...
Risk assessments can be done in individual cases, including in patient and physician interactions. [4] In the narrow sense chemical risk assessment is the assessment of a health risk in response to environmental exposures. [5]
Risk management is the identification, evaluation, and prioritization of risks, [1] followed by the minimization, monitoring, and control of the impact or probability of those risks occurring. [2]
Relative risk is commonly used to present the results of randomized controlled trials. [5] This can be problematic if the relative risk is presented without the absolute measures, such as absolute risk, or risk difference. [6]
Firefighters are exposed to risks of fire and building collapse during their work.. In simple terms, risk is the possibility of something bad happening. [1] Risk involves uncertainty about the effects/implications of an activity with respect to something that humans value (such as health, well-being, wealth, property or the environment), often focusing on negative, undesirable consequences. [2]
Data (/ ˈ d eɪ t ə / DAY-tə, US also / ˈ d æ t ə / DAT-ə) are a collection of discrete or continuous values that convey information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted formally.
Data augmentation is a statistical technique which allows maximum likelihood estimation from incomplete data. [1] [2] Data augmentation has important applications in Bayesian analysis, [3] and the technique is widely used in machine learning to reduce overfitting when training machine learning models, [4] achieved by training models on several slightly-modified copies of existing data.