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Central limit theorem (CLT) is considered an important topic in statistics, because it serves as the basis for subsequent learning in other crucial concepts such as hypothesis testing and power analysis. There is an increasing popularity in using dynamic computer software for illustrating CLT. Graphical displays do not necessarily clear up misconceptions related to this theorem. Many interactive computer simulations allow users to explore the programs in a "what-if" manner. However, users may further build up other misconceptions when they start with unclear concepts of the components that contribute to CLT. This paper analyzes common misconceptions in each component of CLT and evaluates the appropriateness of use of computer simulation. CLT states that a sampling distribution, which is the distribution of the means of random samples drawn from a population, becomes closer to normality as the sample size increases, regardless of the shape of the distribution. Misconceptions are found about the following areas: (1) randomness and random sampling; (2) relationships among sample, population, and sampling distribution; (3) normality; (4) parameters of the sampling distribution; and (5) relationships between the sampling distribution and hypothesis testing. (Contains 31 references.) (Author/SLD)

Descriptors: Computer Simulation, Computer Software, Hypothesis Testing, Identification, Power (Statistics), Sampling, Statistical Distributions











Autor: Yu, Chong Ho; And Others

Fuente: https://eric.ed.gov/?q=a&ft=on&ff1=dtySince_1992&pg=10696&id=ED395989



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