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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/improving-statistical-questions
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课程名称：改进统计问题 概述：本课程旨在帮助您在进行实证研究时提出更好的统计问题。我们将讨论如何设计信息丰富的研究，无论您的预测是正确还是错误。我们将质疑常规，反思如何改进研究实践，以提出更有趣的问题。在实际的作业中，您将学习可以立即应用于自己研究的技术和工具，诸如思考您感兴趣的最小效应大小、为样本量提供合理依据、在考虑发表偏倚的情况下评估文献中的发现、进行元分析，并使您的分析具备可计算的重复性。 如果有时间，建议您在注册本课程之前完成我的课程《改进统计推断》，虽然本课程也是完全自足的。 课程大纲： 模块1：改进您的统计问题 描述：大多数研究人员可以显著提高的一个方面是更清晰地具体化他们的统计问题。进行研究时，您真正想知道的是什么？我们可以提出哪些不同类型的问题？假设检验实际上回答了哪个问题，这个答案是否真的是您感兴趣的，还是您所提的问题更多关于探索、描述或预测的？我们如何能够做出比零假设检验更冒险的预测，且这有何意义？ 模块2：证伪预测 描述：如果您的预测永远无法错误，那么做出预测就没有意义。那么我们如何确保您的预测是可以证伪的？我们讨论了证伪预测的重要性，以及如何在实践中使预测可证伪。证伪预测的一个重要方面是指定一个未预测值的范围，我们将考察指定感兴趣的最小效应大小的不同方法。 模块3：设计信息丰富的研究 描述：如果研究是为了回答一个问题，您需要确保在收集数据后得到的答案是信息丰富的。我们学习为什么能够为错误率提供合理依据非常重要，而不是盲目设定第一类和第二类错误率，并讨论如何做到这一点。我们讨论使用最小效应大小进行功效分析的好处，以及学习模拟数据作为有用工具的理由。模拟可以帮助您提高对统计的理解，使您能够设计信息丰富的研究，甚至提出新问题。 模块4：元分析与偏倚检测 描述：遗憾的是，我们在科学研究中工作，发表的文献并未反映真实的研究。发表偏倚和选择偏倚导致的科学文献在不考虑这些偏见的情况下是无法解释的。我们将讨论真实研究线的样子，以及如何在考虑偏见的情况下进行文献的元分析评估。 模块5：可计算重复性、科学哲学与科学诚信 描述：我们讨论最后的三个主题。首先，我们确保他人可以使用您的数据提出新问题，确保您的数据分析是可计算重复的。接着，我们反思您的科学哲学如何影响您提出的问题类型以及您在研究中所重视的内容。最后，我们讨论科学诚信，反思为什么我们的研究实践并不总是与提供可靠科学问题答案的最佳方法相一致。 模块6：期末考试 描述：该模块包含一场分级考试，涵盖整个课程的内容。我们建议在完成所有其他模块后再进行该考试。
Name:Module 1: Improving Your Statistical Questions
Description:One of the biggest improvements most researchers can make is to more clearly specify their statistical questions. When you perform a study, what is it you really want to know? What are different types of questions we can ask? Which question does a hypothesis test really answer, and is this answer actually what you are interested in, or is the question you are asking more about exploration, description, or prediction? How can we make riskier predictions than null-hypothesis tests, and why is this useful?
Name:Module 2: Falsifying Predictions
Description:There is little use in making predictions if you can never be wrong - so how do we make sure your predictions are falsifiable? We discuss why falsifiable predictions are important, and how to make your predictions falsifiable in practice. One important aspect of making predictions falsifiable is to specify a range of values that is not predicted, and we will examine different approaches to specifying a smallest effect size of interest.
Name:Module 3: Designing Informative Studies
Description:If studies are designed to answer a question, you should make sure the answer you will get after collecting data is informative. Instead of mindlessly setting Type 1 and Type 2 error rates, we will learn why it is important to be able to justify error rates, and some approaches how to do so. We discuss the benefits of using your smallest effect size of interest in power analyses, and why learning to simulate data is a useful tool. Simulations can help you to improve your understanding of statistics, enable you to design informative studies, and even ask novel questions.
Name:Module 4: Meta-Analysis and Bias Detection
Description:Regrettably we work in a scientific enterprise where the published literature does not reflect real research. Publication bias and selection biases lead to a scientific literature that can’t be interpreted without taking these biases into account. We will discuss what real research lines look like, and how to meta-analytically evaluate the literature while keeping bias in mind.
Name:Module 5: Computational Reproducibility, Philosophy of Science, and Scientific Integrity
Description:We discuss three last topics. First, we will make sure other people can use your data to ask new questions, by making sure your data analysis is computationally reproducible. Then, we will reflect on how your philosophy of science influences the types of questions you will ask, and what you value as you do research. Finally, we discuss scientific integrity, and reflect on why our research practice is not always aligned with the best possible ways to provide reliable answers to scientific questions.
Name:Module 6: Final Exam
Description:This module contains a graded exam. It covers content from the entire course. We recommend making this exam only after you went through all the other modules.
This course aims to help you to ask better statistical questions when performing empirical research. We will discuss how to design informative studies, both when your predictions are correct, as when your predictions are wrong. We will question norms, and reflect on how we can improve research practices to ask more interesting questions. In practical hands on assignments you will learn techniques and tools that can be immediately implemented in your own research, such as thinking about the smallest effect size you are interested in, justifying your sample size, evaluate findings in the literature while keeping publication bias into account, performing a meta-analysis, and making your analyses computationally reproducible. If you have the time, it is recommended that you complete my course 'Improving Your Statistical Inferences' before enrolling in this course, although this course is completely self-contained.
