Uncertainty Guide, Meaning , Facts, Information and Description
Uncertainty is an inevitable part of the assertion of knowledge, see Bayesian probability.Mathematicians handle uncertainty using probability theory, Dempster-Shafer theory, fuzzy logic. See also probability.
Examples where uncertainty is important:
- Investing in financial markets such as the stock market.
- Uncertainty is designed into games, most notably in gambling, where chance is central to play.
- In physics in certain situations, uncertainty has been elevated into a principle, the uncertainty principle.
- In weather forcasting it is now commonplace to include data on the degree of uncertainty in a weather forecast.
- Uncertainty is often an important factor in economics. According to economist Frank Knight, it is different from risk, where there is a specific probability assigned to each outcome (as when flipping a fair coin). Uncertainty involves a situation that has unknown probabilities, while the estimated probabilities of possible outcomes need not add to unity.
- In metrology, measurement uncertainty is a central concept quantifying the dispersion one may reasonably attribute to a measurement result. In daily life, measurement uncertainty is often implicit ("He is 6 feet tall" give or take a few inches), while for any serious use an explicit statement of the measurement uncertainty is necessary. The expected measurement uncertainty of many measuring instruments (scales, oscilloscopes, force gages, rulers, thermometers, etc) is often stated in the manufacturers specification. The most commonly used procedure for calculating measurement uncertainty is described in the Guide to the Expression of Uncertainty in Measurement (often referred to as "the GUM") published by ISO. A derived work is for example the National Institute for Standards and Technology (NIST) publication NIST Technical Note 1297 "Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results". The uncertainty of the result of a measurement generally consists of several components. The components are regarded as random variables, and may be grouped into two categories according to the method used to estimate their numerical values:
- those which are evaluated by statistical methods,
- those which are evaluated by other means, e.g. by assigning a probability distribution.
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