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Lane, David M. – Journal of Statistics Education, 2015
Recently Watkins, Bargagliotti, and Franklin (2014) discovered that simulations of the sampling distribution of the mean can mislead students into concluding that the mean of the sampling distribution of the mean depends on sample size. This potential error arises from the fact that the mean of a simulated sampling distribution will tend to be…
Descriptors: Statistical Distributions, Sampling, Sample Size, Misconceptions
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Watkins, Ann E.; Bargagliotti, Anna; Franklin, Christine – Journal of Statistics Education, 2014
Although the use of simulation to teach the sampling distribution of the mean is meant to provide students with sound conceptual understanding, it may lead them astray. We discuss a misunderstanding that can be introduced or reinforced when students who intuitively understand that "bigger samples are better" conduct a simulation to…
Descriptors: Simulation, Sampling, Sample Size, Misconceptions
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Gilliland, Dennis; Melfi, Vince – Journal of Statistics Education, 2010
Confidence interval estimation is a fundamental technique in statistical inference. Margin of error is used to delimit the error in estimation. Dispelling misinterpretations that teachers and students give to these terms is important. In this note, we give examples of the confusion that can arise in regard to confidence interval estimation and…
Descriptors: Statistical Inference, Surveys, Intervals, Sample Size
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Smith, Margaret H. – Journal of Statistics Education, 2004
Unless the sample encompasses a substantial portion of the population, the standard error of an estimator depends on the size of the sample, but not the size of the population. This is a crucial statistical insight that students find very counterintuitive. After trying several ways of convincing students of the validity of this principle, I have…
Descriptors: Sample Size, Error of Measurement, Mathematics Instruction, College Mathematics