Random number
In mathematics and statistics, a random number is either Pseudo-random or a number generated for, or part of, a set exhibiting statistical randomness.
In common understanding, it's that all have an equal chance; conversely, none have an advantage.[1]
Algorithms and implementations
A 1964-developed algorithm[2] is popularly known as the Knuth shuffle or the Fisher–Yates shuffle (based on work they did in 1938). A real-world use for this is sampling water quality in a reservoir.
Common understanding
In common understanding, "1 2 3 4 5" is not as random as "3 5 2 1 4" and certainly not as random as "47 88 1 32 41" but "we can't say authoritavely that the first sequence is not random ... it could have been generated by chance."[3]
When a police officer claims to have done a "random .. door-to-door" search, there is a certain expectation that members of a jury will have. [4][5]
Real world consequences
Flaws in randomness have real-world consequences.[6][1]
A 99.8% randomness was shown by researchers to negatively affect an estimated 27,000 customers of a large service[6] and that the problem was not limited to just that situation.
See also
References
- ^ a b Reid Forgrave (May 3, 2018). "The man who cracked the lottery". New York Times.
- ^ Richard Durstenfeld (July 1964). "Algorithm 235: Random permutation". Communications of the ACM (Association for Computing Machinery). Vol. 7, no. 7. p. 420. doi:10.1145/364520.364540.
- ^ Jonathan Knudson (January 1998). "Javatalk: Horseshoes, hand grenades and random numbers". Sun Server. pp. 16–17.
- ^ Tom Hays (April 16, 1995). "NYPD Bad Cop's Illegal Search Mars Career". Los Angeles Times.
- ^ A pre-compiled list of apartment numbers would be a violation thereof.
- ^ a b John Markoff (February 14, 2012). "Flaw Found in an Online Encryption Method". New York Times.
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