Simulations of the Sampling Distribution of the Mean Do Not Necessarily Mislead and Can Facilitate Learning
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 si...
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| Veröffentlicht in: | Journal of statistics and data science education 2015-07, Vol.23 (2) |
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| creator | Lane, David M. |
| description | 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 closer to the population mean with large sample sizes than it will with small sample sizes. Although this pattern does not change as a function of the number of samples, the size of the difference between simulated sampling distribution means does and can be made invisible to observers by using a very large number of samples. It is now practical for simulations to use these very large numbers of samples since the speed of computers and even mobile devices is sufficient to simulate a sampling distribution based on 1,000,000 samples in just a few seconds. Research on the effectiveness of sampling distribution simulations is briefly reviewed and it is concluded that they are effective as long as they are used in a pedagogically sound manner. |
| doi_str_mv | 10.1080/10691898.2015.11889738 |
| format | Article |
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| ispartof | Journal of statistics and data science education, 2015-07, Vol.23 (2) |
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| language | eng |
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| source | Research Library; Research Library (Alumni Edition); DOAJ Directory of Open Access Journals; Research Library Prep; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Alma/SFX Local Collection; ProQuest Central |
| subjects | Central Limit theorem Effectiveness of simulations Error of Measurement Instructional Effectiveness Misconceptions Sample Size Sampling Sampling distribution simulations Simulation Statistical Distributions Variance of means |
| title | Simulations of the Sampling Distribution of the Mean Do Not Necessarily Mislead and Can Facilitate Learning |
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