Skip to main content

Normal Distribution, Univariate

  • Reference work entry
  • First Online:

Brief Historical Background

The normal distribution is used extensively in probability theory, statistics, and the natural and social sciences. It is also called the Gaussian distribution because Carl Friedrich Gauss (1809) used it to analyze astronomical data. The normal distribution was (a) first introduced by Abraham de Moivre (1733), as an approximation to a binomial distribution (b) used by Laplace (1774), as an approximation to hypergeometric distribution to analyze errors of experiments and (c) employed, in the past, by Legendre, Peirce, Galton, Lexis, Quetelet, etc. The normal distribution can be used as an approximation to other distributions because the standardized sum of a large number of independent and identically distributed random variables is approximately normally distributed. Thus, the normal distribution can be used when a large number of non-normal distribution correspond more closely to observed values. The 1816 work of Gauss (Gauss 1816) is the earliest result...

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   1,100.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   549.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References and Further Reading

  • De Moivre A (1733) Approximatio ad Summam Ferminorum Binomii \({(\textrm{ a} + \textrm{ b})}^{\textrm{ n}}\) in Seriem expansi. Supplementum II to Miscellanae Analytica, pp 1–7

    Google Scholar 

  • Gauss CF (1809) Theoria Motus Corporum Coelestum. Perthes and Besser, Hamburg

    Google Scholar 

  • Gauss CF (1816) Bestimmung der Genauigkeit der Beobachtungen. Z Astronom 1:185–197

    Google Scholar 

  • Gnedenko BV, Kolmogorov AN (1954) Limit distributions for sums of independent random variables. Addison-Wesley, Reading

    MATH  Google Scholar 

  • Johnson NL, Kotz S, Balakrishnan N (1994) Continuous univariate distributions, vol 1. Wiley, New York

    MATH  Google Scholar 

  • Laplace PS (1774) Determiner le milieu que l’on doit prendre entre trois obsevations données d’un même phénomené. In: Mémoires de Mathématique et Physique presentées à l’Académie Royale dês Sciences par divers Savans, vol 6, pp 621–625

    Google Scholar 

  • Lindeberg JW (1922) Eine neue Herleitung des Exponentialgesetzes in der Wahrscheinlichkeitsrechnung. Math Z 15:211–225

    MATH  MathSciNet  Google Scholar 

  • Loève M (1963) Probability theory 3rd edn. D. Van Nostrand, New York

    Google Scholar 

  • Luke YL (1969) The special functions and their approximations, vol 1. Academic, New York

    MATH  Google Scholar 

  • Lyapunov A (1900) Sur une proposition de la théorie des probabilités. Izv Akad Nauk SSSR Ser V 13:359–386

    MATH  Google Scholar 

  • Rathie PN, Swamee PK, Matos GG, Coutinho M, Carrijo TB (2008) H-functions and statistical distributions. Ganita 59(2):23–37

    MATH  Google Scholar 

  • Rubinstein RY, Kroese DP (2008) Simulation and Monte Carlo method. Wiley, New York

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this entry

Cite this entry

Rathie, P.N. (2011). Normal Distribution, Univariate. In: Lovric, M. (eds) International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04898-2_425

Download citation

Publish with us

Policies and ethics