Uncertainty
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 probablities of possible outcomes need not add to unity.
- In metrology,uncertainty is built into all measuring instruments (scales, oscilloscopes, force gages, rulers, thermometers, etc). The procedure for calculating measurement uncertainty has been documented by the National Institute for Standards and Technology (NIST) in their publication NIST Technical Note 1297 "Guidelines for Evaluating and Expressing the Uncertinty of NIST Measurement Results". The uncertainty of the result of a measurement generally consists of several components which 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.
Further reading
- by Ames Glanz, July 31, 2002
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