Jump to content

Normal distribution

From Simple English Wikipedia, the free encyclopedia
Normal
Probability density function
Probability density function for the normal distribution
The green line is the standard normal distribution
Cumulative distribution function
Cumulative distribution function for the normal distribution
Colors match the image above
Parameters location (real)
squared scale (real)
Support
Probability density function (pdf)
Cumulative distribution function (cdf)
Mean
Median
Mode
Variance
Skewness 0
Excess kurtosis 0
Entropy
Moment-generating function (mgf)
Characteristic function
For the normal distribution, the values less than one standard deviation away from the mean account for 68.27% of the set; while two standard deviations from the mean account for 95.45%; and three standard deviations account for 99.73%.

The normal distribution is a probability distribution used in probability theory and statistics. It is also called Gaussian distribution because it was first discovered by Carl Friedrich Gauss. The normal distribution is very important in many fields because many things take this form.[1] A random variable that takes this form is normally distributed, and can be called a normal deviate. The normal distributed is often called the bell curve, because the graph of its probability density looks like a bell. The standard normal distribution (also known as the Z distribution) is a normal distribution that has a mean of zero and a standard deviation of one.[2]

The form of the distribution is

In a normal distribution, the parameter is the mean ("average"). The standard deviation ("variability") is .[2] The variance of the distribution is .

The normal distribution is important because it can represent real-life examples. It is used in natural and social sciences.[3][4] Some examples include:

  • Height
  • Test scores
  • Measurement errors
  • Light intensity as in laser light)
  • Insurance companies use normal distributions to model certain average cases.[5]

The central limit theorem can be used to describe real-life data as a normal distribution.[6]

Origin[change | change source]

Its origin goes back to the 18th century to De Moivre and in the early 19th century with Gauss.

Use[change | change source]

It is the most widely used piece of statistics by far. It was famously used in World War I by the United States Army to decide when men were so poor mentally that they could not be used by the Army in any job.

Related pages[change | change source]

References[change | change source]

  1. "Normal Distribution | Data Basecamp". 2021-11-26. Retrieved 2022-07-15.
  2. 2.0 2.1 "List of Probability and Statistics Symbols". Math Vault. 2020-04-26. Retrieved 2020-08-15.
  3. "Normal Distribution - easily explained! | Data Basecamp". 2021-11-26. Retrieved 2023-05-29.
  4. Weisstein, Eric W. "Normal Distribution". mathworld.wolfram.com. Retrieved 2020-08-15.
  5. "Normal Distribution". www.mathsisfun.com. Retrieved 2020-08-15.
  6. Kwak, Sang Gyu; Kim, Jong Hae (2017-02-21). "Central limit theorem: the cornerstone of modern statistics". National Library of Medicine. Retrieved 2024-05-30.

Other websites[change | change source]