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- W4232180982 startingPage "1027" @default.
- W4232180982 abstract "This chapter describes the use of the normal distribution to approximate the probabilities of interest and several versions of the central limit theorem (CLT). This theorem ensures that, under fairly mild conditions, the distribution of a suitably standardized sum of random variables approaches a normal distribution as the number of random variables that are summed increases. The scope of the CLT is broadened substantially by two additional theoretical results, namely the delta method and Slutsky's theorem. These two results discuss various applications of the CLT. The primary use of the CLT is to approximate the sampling distribution of a test statistic or the sampling distribution of an asymptotically pivotal quantity. It is frequentists rather than Bayesians who use sampling distributions to make inferences about parameter values. Normal approximations are applied to two areas in which randomization justifies statistical inference, namely estimating the mean of a finite population (random selection) and testing hypotheses about equality of population distributions (random assignment)." @default.
- W4232180982 created "2022-05-12" @default.
- W4232180982 creator A5053753399 @default.
- W4232180982 date "2011-01-01" @default.
- W4232180982 modified "2023-10-14" @default.
- W4232180982 title "Normal Approximations" @default.
- W4232180982 doi "https://doi.org/10.1016/b978-0-444-51862-0.50032-0" @default.
- W4232180982 hasPublicationYear "2011" @default.
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