Law of truly large numbers

File:Bernoulli trial sequence.svgs, and 1 − P  vs n. As n increases, the probability of a 1/n-chance event never appearing after n tries rapidly {{nowrap|converges to {{math|1/e}}.}}]]

For a simplified example of the law, assume that a given event happens with a probability for its occurrence of 0.1%, within a single trial. Then, the probability that this so-called unlikely event does not happen (improbability) in a single trial is 99.9% (0.999).

For a sample of only 1,000 independent trials, however, the probability that the event does not happen in any of them, even once (improbability), is onlyhere other law of "Improbability principle" also acts - the "law of probability lever", which is (according to David Hand) a kind of butterfly effect: we have a value "close" to 1 raised to large number what gives "surprisingly" low value or even close to zero if this number is larger, this shows some philosophical implications, questions the theoretical models but it does not render them useless - evaluation and testing of theoretical hypothesis (even when probability of it correctness is close to 1) can be its falsifiability - feature widely accepted as important for the scientific inquiry which is not meant to lead to dogmatic or absolute knowledge, see: statistical proof. 0.999¹⁰⁰⁰ ≈{{nbs}}0.3677, or 36.77%. Then, the probability that the event does happen, at least once, in 1,000 trials is {{nowrap|({{hsp}}1 − }}0.999¹⁰⁰⁰ ≈{{nbs}}0.6323, {{nowrap|or{{hsp}})}} 63.23%. This means that this "unlikely event" has a probability of 63.23% of happening if 1,000 independent trials are conducted. If the number of trials were increased to 10,000, the probability of it happening at least once in 10,000 trials rises to {{nowrap|({{hsp}}1 − }}0.999¹⁰⁰⁰⁰ ≈{{nbs}}0.99995, {{nowrap|or{{hsp}})}} 99.995%. In other words, a highly unlikely event, given enough independent trials with some fixed number of draws per trial, is even more likely to occur.

For an event X that occurs with very low probability of 0.0000001%, or once in one billion trials, in any single sample (see also almost never), considering 1,000,000,000 as a "truly large" number of independent samples gives the probability of occurrence of X equal to {{nowrap|1=1 − 0.999999999^1000000000 ≈ 0.63 = 63%}} and a number of independent samples equal to the size of the human population (in 2021) gives probability of event X: {{nowrap|1=1 − 0.999999999^7900000000 ≈ 0.9996 = 99.96%.}}[https://www.desmos.com/calculator/r3nmu0qxli Graphing calculator] at Desmos (graphing)

These calculations can be formalized in mathematical language as: "the probability of an unlikely event X happening in N independent trials can become arbitrarily near to 1, no matter how small the probability of the event X in one single trial is, provided that N is truly large."Proof in: Elemér Elad Rosinger, (2016), [https://hal.archives-ouvertes.fr/hal-01386163/document "Quanta, Physicists, and Probabilities ... ?"] page 28

For example, where the probability of unlikely event X is not a small constant but decreased in function of N, see graph.

In high availability systems even very unlikely events have to be taken into consideration, in series systems even when the probability of failure for single element is very low after connecting them in large numbers probability of whole system failure raises (to make system failures less probable redundancy can be used - in such parallel systems even highly unreliable redundant parts connected in large numbers raise the probability of not breaking to required high level).[https://books.google.com/books?id=aMGQDwAAQBAJ&pg=PA33 Reliability of Systems in Concise Reliability for Engineers], Jaroslav Menčík, 2016

The law comes up in criticism of pseudoscience and is sometimes called the Jeane Dixon effect (see also Postdiction). It holds that the more predictions a psychic makes, the better the odds that one of them will "hit". Thus, if one comes true, the psychic expects us to forget the vast majority that did not happen (confirmation bias).1980, Austin Society to Oppose Pseudoscience (ASTOP) distributed by ICSA (former American Family Foundation) [http://www.icsahome.com/elibrary/studyguides/education/astoppsychicdetectives "Pseudoscience Fact Sheets, ASTOP: Psychic Detectives"] Humans can be susceptible to this fallacy.

Another similar manifestation of the law can be found in gambling, where gamblers tend to remember their wins and forget their losses,Daniel Freeman, Jason Freeman, 2009, London, "Know Your Mind: Everyday Emotional and Psychological Problems and How to Overcome Them" p. 41 even if the latter far outnumber the former (though depending on a particular person, the opposite may also be true when they think they need more analysis of their losses to achieve fine tuning of their playing systemMikal Aasved, 2002, Illinois, The Psychodynamics and Psychology of Gambling: The Gambler's Mind vol. I, p. 129). Mikal Aasved links it with "selective memory bias", allowing gamblers to mentally distance themselves from the consequences of their gambling by holding an inflated view of their real winnings (or losses in the opposite case – "selective memory bias in either direction").

{{Columns-list|colwidth=35em|

• Black swan theory
• Boltzmann brain
• Bonferroni correction
• Coincidence
• Infinite monkey theorem
• Junkyard tornado
• Law of large numbers
• Law of small numbers
• Library of Babel
• Littlewood's law
• Look-elsewhere effect
• Miracle
• Murphy's Law
• Poisson clumping
• Psychic phenomena
• Totalitarian principle

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{{Reflist}}

• {{MathWorld|LawofTrulyLargeNumbers}}
• {{cite journal |last1=Diaconis |first1=P. |author-link1=Persi Diaconis |last2=Mosteller |first2=F. |author-link2=Frederick Mosteller |title=Methods of Studying Coincidences |journal=Journal of the American Statistical Association |volume=84 |issue=408 |pages=853–61 |year=1989 |mr=1134485 |url=http://stat.stanford.edu/~cgates/PERSI/papers/mosteller89.pdf |access-date=2009-04-28 |doi=10.2307/2290058 |jstor=2290058 |url-status=dead |archiveurl=https://web.archive.org/web/20100712091914/http://stat.stanford.edu/~cgates/PERSI/papers/mosteller89.pdf |archivedate=2010-07-12 }}
• {{cite book |last= Everitt |first= B.S. |year= 2002 |title= Cambridge Dictionary of Statistics |edition= 2nd |isbn= 978-0521810999 }}
• David J. Hand, (2014), [https://books.google.com/books?id=raZRAQAAQBAJ The Improbability Principle: Why Coincidences, Miracles, and Rare Events Happen Every Day]

• [http://www.scientificamerican.com/article/math-explains-likely-long-shots-miracles-and-winning-the-lottery/ Math Explains Likely Long Shots, Miracles and Winning the Lottery (Excerpt)] in Scientific American by David Hand 2014
• [http://skepdic.com/lawofnumbers.html skepdic.com on the Law of Truly Large Numbers]
• [http://www.quackwatch.org/04ConsumerEducation/coincidence.html on the Law of Truly Large Numbers]
• [https://oeis.org/A219330 The On-Line Encyclopedia of Integer Sequences] – related integer sequence

Category:Probability theory

Category:Statistical laws