Format

Send to

Choose Destination
Korean J Anesthesiol. 2017 Apr;70(2):144-156. doi: 10.4097/kjae.2017.70.2.144. Epub 2017 Feb 21.

Central limit theorem: the cornerstone of modern statistics.

Author information

1
Department of Medical Statistics, School of Medicine, Catholic University of Daegu, Daegu, Korea.
2
Department of Anesthesiology and Pain Medicine, School of Medicine, Catholic University of Daegu, Daegu, Korea.

Abstract

According to the central limit theorem, the means of a random sample of size, n, from a population with mean, µ, and variance, σ2, distribute normally with mean, µ, and variance, [Formula: see text]. Using the central limit theorem, a variety of parametric tests have been developed under assumptions about the parameters that determine the population probability distribution. Compared to non-parametric tests, which do not require any assumptions about the population probability distribution, parametric tests produce more accurate and precise estimates with higher statistical powers. However, many medical researchers use parametric tests to present their data without knowledge of the contribution of the central limit theorem to the development of such tests. Thus, this review presents the basic concepts of the central limit theorem and its role in binomial distributions and the Student's t-test, and provides an example of the sampling distributions of small populations. A proof of the central limit theorem is also described with the mathematical concepts required for its near-complete understanding.

KEYWORDS:

Normal distribution; Probability; Statistical distributions; Statistics

Supplemental Content

Full text links

Icon for Publishing M2Community Icon for PubMed Central
Loading ...
Support Center