Zipf's law
Originally the term Zipf's law meant the observation of Harvard linguist George Kingsley Zipf (SAMPA: [zif]) that the frequency of use of the nth-most-frequently-used word in any natural language is approximately inversely proportional to n.
Mathematically, it is impossible for Zipf's law to hold exactly if there are infinitely many words in a language, since for any constant of proportionality c > 0, the sum of all relative frequencies must be
As long as the exponent s exceeds 1, it is possible for such a law to hold with infinitely many words, since if s > 1 then
The term Zipf's law has consequently come to be used to refer to frequency distributions of "rank data" in which the relative frequency of the nth-ranked item is given by the Zeta distribution, 1/(nsζ(s)), where s > 1 is a parameter indexing this family of probability distributions. Indeed, the term Zipf's law sometimes simply means the zeta distribution, since probability distributions are sometimes called "laws". This distribution is sometimes called the Zipfian distribution or Yule distribution.
A more general law proposed by Benoit Mandelbrot has frequencies
Zipf's law is an experimental law, not a theoretical one. The causes of Zipfian distributions in real life are a matter of some controversy. However, Zipfian distributions are commonly observed in many kinds of phenomena.
Zipf's law is often demonstrated by scatterplotting the data, with the axes being log(rank order) and log(frequency). If the points are close to a single straight line, the distribution follows Zipf's law.
Examples of collections approximately obeying Zipf's law:
- frequency of accesses to web pages
- in particular the access counts on the Wikipedia page, with b approximately equal to 0.3
- page access counts on Polish Wikipedia (data for late July 2003) approximately obey Zipf's law with b about 0.5
- words in the English language
- for instance, in Shakespeare's play Hamlet, with b approximately 0.5, see Shakespeare word frequency lists
- sizes of settlements
- income distribution amongst individuals
- size of earthquakes
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