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Benefit-Risk
Published in Ken Holmes, Marcus Elkington, Phil Harris, Clark's Essential Physics in Imaging for Radiographers, 2021
There are a number of ways of describing the risk. These include:Equivalent background dose, expressed in equivalent period of natural background radiation, e.g. a few days to several years.Statistical risk, expressed in numbers, e.g. risk of cancer is 1 in 1,000,000.Comparisons to general risks of cancer, i.e. the population have a 1 in 2 chance of getting cancer.Comparison to everyday activities:For example, airline flights are very safe with the risk of a crash being well below 1 in 1,000,000.A chest X-ray exposes you to the same risk as a 4-hour flight.Smoking or drinking alcohol.Driving or undertaking dangerous sports, such as skydiving.Lost life expectancy, given in days.
Six Sigma
Published in James William Martin, Operational Excellence, 2021
Table 9.15 describes statistical risk. Alpha risk is related to a decision of correctly rejecting a false null hypothesis, whereas beta risk is failing to reject a false null hypothesis. As an example, if a null hypothesis is true but we do not reject it, then we have made a Type I decision error. Alternatively, if a null hypothesis is false but we do not reject it, then we have made a Type II error. Statistical risk occurs because samples are used to estimate population parameters such as a mean or variance.
Organizational Learning in Air Safety: The Role of the Different Stakeholders
Published in José Sánchez-Alarcos, Aviation and Human Factors, 2019
In summary, technical and statistical risk is managed. Although there are some flaws, it could be added that risk is, in general terms, properly managed. Social and organizational risk are not, since many players are not even conscious of its existence or of the changes in the environment, especially regarding the feasibility of controlling the flow of information.
State-of-the-Art in Evaluation Approaches for Risk Assessment of Insider Threats to Nuclear Facility Physical Protection Systems
Published in Nuclear Science and Engineering, 2023
Chris Faucett, Karen Vierow Kirkland
Starr et al. define four types of future risk,8 which are restated by Gough as follows9: Real risk determined eventually by future circumstances when they develop fully.Statistical risk determined by currently available data, typically measured actuarily.Predicted risk predicted analytically from systems models structured from historical dataPerceived risk seen intuitively by individuals.
Estimated Radiation Doses and Projected Cancer Risks for New Mexico Residents from Exposure to Radioactive Fallout from the Trinity Nuclear Test
Published in Nuclear Technology, 2021
Steven L. Simon, André Bouville, Harold L. Beck
Cahoon et al.5 used published resources to characterize the size of the New Mexico population and baseline cancer rates with established statistical risk projection methods and reconstructed radiation doses7 to estimate the potential magnitude and proportion of radiation-related cancer risks. The number of persons (and percentages of the total) estimated to have been alive in New Mexico in 1945 were estimated from the 1940 and 1950 U.S. Census reports. Based on those data, our estimates of the population resident in New Mexico in 1945 was about 315 000 Whites (54%), 226 000 Hispanics (39%), 35 000 Native Americans (5.9%), and about 6000 (1.0%) African Americans, totaling approximately 581 000 residents of New Mexico. The age distribution and other data can be found in Table 1 of Cahoon et al.5
On the estimation of the size of a subgroup in industrial production: The test gate method
Published in Quality Engineering, 2019
Lennart Kann, Daniel Herrmann, Rainer Göb
As mentioned in Section “Introduction”, a statistical risk analysis contains the two steps of estimating the volume with deviation and of estimating the effect of the deviation on the lifetimes of the affected volume. This was also illustrated in Section “Technical quality and usage quality”, where technical and usage quality were distinguished and the effect of the technical deviation on the lifetime was described by a proportional hazards model. Relative to the second step, many applications of prediction intervals consider lifetime problems, where due to censoring usually only approximate solutions exist. Literature focuses on different procedures to obtain prediction intervals and their respective coverage properties. These approaches usually improve upon the naive (or plug-in) estimate by calibration (Escobar and Meeker 1999; Lawless and Fredette 2005). The focus of the application is on reliability either in lifetime testing or warranty analysis.