Tools for Teaching and Assessing Statistical Inference

许多统计推理涉及将数据和机会的概念结合起来，以执行推理过程并解释结果。这一推理的基础是对一些重要概念的概念性理解，如概率、可变性、样本、样本分布和正态分布。虽然统计学课程教授这一内容，但学生在对真实的统计问题进行推理或解释研究调查结果时，往往无法完全整合和应用这些想法。这个项目将开发和传播帮助学生学习统计推断背后的核心概念的材料。具体地说，我们将创建使用模拟软件的教学模块，帮助学生发展抽象概念，如抽样分布、可信区间、p值和幂。在我们之前研究的基础上，我们将在一个概念变化模型上建立这些模块，在这个模型中，学生首先做出预测，然后使用模拟软件进行测试。这些单元还将包括用于评估必要的概念知识的评估工具和用于评估对作为统计推断基础的统计概念的理解程度的工具。所有材料(包括我们的模拟软件程序)都将在万维网上提供给统计教育工作者。

Much of statistical reasoning involves combining ideas of data and chance to carry out inferential procedures and interpret results. Underlying this reasoning is a conceptual understanding of some important ideas, such as probability, variability, samples, sampling distributions, and the normal distribution. Although statistics courses teach this content, students are often unable to fully integrate and apply these ideas when reasoning about real statistical problems or when interpreting the results of research investigations. This project will develop and disseminate materials that help students learn the core concepts underlying statistical inference. Specifically, we will create instructional modules that use simulation software to help students develop abstract concepts such as sampling distribution, confidence interval, p-values, and power. Based on our previous research, we will build the modules on a model of conceptual change, where students first make predictions and then test them, using the simulation software. These modules will also include assessment instruments to assess prerequisite conceptual knowledge and instruments to assess understanding of the statistical concepts underlying statistical inference. All materials (including our simulation software programs) will be made available to statistics educators on the World Wide Web.