Central Limit Theorem

Carolyn J. Anderson

doi:10.1002/9780470479216.corpsy0160

Central Limit Theorem

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Abstract

Many statistical inferential procedures such as hypothesis testing and the estimation of confidence intervals are based on the assumption that the distribution of a sample statistic is normal. The Central Limit Theorem (CLT) often justifies the assumption that the distribution of a sample statistic (e.g., mean, sum score, and test statistic) is normal. The Central Limit Theorem states that, for a large sample of n observations from a population with a finite mean and variance, the sampling distribution of the sum or mean of samples of size n is approximately normal.

Original language	English (US)
Title of host publication	The Corsini Encyclopedia of Psychology
Editors	Irving B. Weiner, W. Edward Craighead
Publisher	John Wiley & Sons, Ltd.
ISBN (Electronic)	9780470479216
DOIs	https://doi.org/10.1002/9780470479216.corpsy0160
State	Published - Jan 30 2010

Keywords

mean
sum
population
sampling distribution
normal distribution

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10.1002/9780470479216.corpsy0160

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title = "Central Limit Theorem",

abstract = "Many statistical inferential procedures such as hypothesis testing and the estimation of confidence intervals are based on the assumption that the distribution of a sample statistic is normal. The Central Limit Theorem (CLT) often justifies the assumption that the distribution of a sample statistic (e.g., mean, sum score, and test statistic) is normal. The Central Limit Theorem states that, for a large sample of n observations from a population with a finite mean and variance, the sampling distribution of the sum or mean of samples of size n is approximately normal.",

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N2 - Many statistical inferential procedures such as hypothesis testing and the estimation of confidence intervals are based on the assumption that the distribution of a sample statistic is normal. The Central Limit Theorem (CLT) often justifies the assumption that the distribution of a sample statistic (e.g., mean, sum score, and test statistic) is normal. The Central Limit Theorem states that, for a large sample of n observations from a population with a finite mean and variance, the sampling distribution of the sum or mean of samples of size n is approximately normal.

AB - Many statistical inferential procedures such as hypothesis testing and the estimation of confidence intervals are based on the assumption that the distribution of a sample statistic is normal. The Central Limit Theorem (CLT) often justifies the assumption that the distribution of a sample statistic (e.g., mean, sum score, and test statistic) is normal. The Central Limit Theorem states that, for a large sample of n observations from a population with a finite mean and variance, the sampling distribution of the sum or mean of samples of size n is approximately normal.

KW - mean

KW - sum

KW - population

KW - sampling distribution

KW - normal distribution

U2 - 10.1002/9780470479216.corpsy0160

DO - 10.1002/9780470479216.corpsy0160

M3 - Chapter

BT - The Corsini Encyclopedia of Psychology

A2 - Weiner, Irving B.

A2 - Craighead, W. Edward

PB - John Wiley & Sons, Ltd.

ER -

Central Limit Theorem

Abstract

Keywords

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