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Towards more accessible conceptions of statistical inference

机译:迈向更易理解的统计推断概念

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There is a compelling case, based on research in statistics education, for first courses in statistical inference to be underpinned by a staged development path. Preferably over a number of years, students should begin working with precursor forms of statistical inference, much earlier than they now do. A side benefit is giving younger students more straightforward and more satisfying ways of answering interesting real world questions. We discuss the issues that are involved in formulating precursor versions of inference and then present some specific and highly visual proposals. These build on novel ways of experiencing sampling variation and have intuitive connections to the standard formal methods of making inferences in first university courses in statistics. Our proposal uses visual comparisons to enable the inferential step to be made without taking the eyes off relevant graphs of the data. This allows the time and conceptual distances between questions, data and conclusions to be minimized, so that the most critical linkages can be made. Our approach was devised for use in high schools but is also relevant to adult education and some introductory tertiary courses.
机译:根据统计学教育的研究,有一个令人信服的案例是,统计推理的第一门课程将由分阶段的发展路径来支撑。最好是在几年后,学生应该比现在更早地开始使用统计推断的前体形式。附带的好处是使年轻的学生更直接,更满意地回答有趣的现实世界问题。我们讨论了制定推理的前体版本时涉及的问题,然后提出了一些具体且高度可视化的建议。这些建立在体验样本变化的新颖方法的基础上,并且与统计的第一门大学课程的推理的标准形式方法有直观的联系。我们的建议使用视觉比较来进行推理,而不会忽视数据的相关图表。这样可以使问题,数据和结论之间的时间和概念上的距离最小化,从而可以建立最关键的联系。我们设计的方法旨在用于高中,但也与成人教育和一些入门级的高等教育课程有关。

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