Comparison of data sets as a precursor to inferential statistics

Minsun Park, Mimi Park, Eun-Sung Ko, K. Lee
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Abstract

Comparing two data sets can be a powerful tool in light of its use toward a consideration of inferential statistics. Both informal and formal statistical reasoning are developed when comparing data sets, which has implications for researchers who investigate ways to help students transfer from informal to formal reasoning. In this paper, we examined students’ reasoning to identify how they treat data value, center, spread, and sample, which are important factors in inferential statistics. Students' understanding of data value, center, and spread were appropriate, but that of sample was not. From the results, we suggest instructional ideas for a task which can connect descriptive and inferential statistics.
作为推论统计的前兆的数据集的比较
比较两个数据集可以是一个强大的工具,因为它用于考虑推理统计。在比较数据集时,非正式和正式的统计推理都得到了发展,这对研究如何帮助学生从非正式推理转移到正式推理的研究人员有影响。在本文中,我们考察了学生的推理能力,以确定他们如何对待数据值、中心、传播和样本,这些都是推理统计中的重要因素。学生对数据价值、中心和传播的理解是恰当的,但对样本的理解是不恰当的。根据结果,我们提出了一项可以将描述性统计和推理统计联系起来的任务的教学思路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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