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- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한마취통증의학회(구 대한마취과학회) Korean Journal of Anesthesiology Korean Journal of Anesthesiology Vol.70 No.2
- 발행연도
- 2017.1
- 수록면
- 144 - 156 (13page)
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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 nearcomplete understanding.
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