Open AccessEffects of teaching the sampling distribution of the means using simulation with and without stating the central limit theorem
Author(s) -
Rini Oktavia,
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Nurmaulidar,
Intan Syahrini,
Hafnani,
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Publication year - 2019
Language(s) - English
Resource type - Conference proceedings
DOI - 10.52041/srap.19412
Subject(s) - central limit theorem , sampling (signal processing) , limit (mathematics) , statistics , sampling distribution , mathematics education , class (philosophy) , distribution (mathematics) , sample (material) , sample size determination , affect (linguistics) , population , mathematics , computer science , psychology , artificial intelligence , demography , mathematical analysis , physics , communication , filter (signal processing) , sociology , computer vision , thermodynamics
Teaching sampling distribution of the means (SDM) using simulation has the potential to mislead students who might falsely believe that the mean of SDM will more closely approximate the population mean (μ) as the sample size (n) increases. A teaching experiment was conducted involving two Introductory Statistics classes. Both classes were taught the concept of SDM using simulation but the central limit theorem (CLT) was only stated in one class. A questionnaire on assessing students’ thinking and possible misunderstanding about CLT and sampling distribution was administered before and after the experiment to both groups. A statistical analysis comparing both groups were conducted. It was found that the instructions affect students’ understanding of SDM, however, there is no significant difference in students’ understanding of sampling distribution for both classes after the instructions.