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Central limit theorem: the cornerstone of modern statistics / 대한마취과학회지
Korean Journal of Anesthesiology ; : 144-156, 2017.
Artículo en Inglés | WPRIM | ID: wpr-34198
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, σ²/n. 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.
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Texto completo: Disponible Índice: WPRIM (Pacífico Occidental) Asunto principal: Distribución Normal / Distribuciones Estadísticas / Conceptos Matemáticos Idioma: Inglés Revista: Korean Journal of Anesthesiology Año: 2017 Tipo del documento: Artículo

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Texto completo: Disponible Índice: WPRIM (Pacífico Occidental) Asunto principal: Distribución Normal / Distribuciones Estadísticas / Conceptos Matemáticos Idioma: Inglés Revista: Korean Journal of Anesthesiology Año: 2017 Tipo del documento: Artículo