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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/statistical-inferences
课程评论：没有评论
课程名称：改善您的统计推断 概述：本课程旨在帮助您从实证研究中进行更好的统计推断。首先，我们将讨论如何正确解读p值、效应大小、置信区间、贝叶斯因子和似然比，以及这些统计数据如何回答您可能感兴趣的不同问题。接下来，您将学习如何设计实验以控制假阳性率，以及如何确定研究的样本大小，以实现较高的统计功效。随后，您将学习如何在科学文献中解读证据，尤其是在普遍存在的发表偏倚背景下，例如学习p-curve分析。最后，我们将讨论科学哲学、理论构建和积累科学的方法，包括如何进行复制研究、如何注册您的实验以及如何遵循开放科学原则分享您的结果。 在实际的动手作业中，您将学习如何模拟t检验以了解可以预期的p值，计算似然比，并了解二项贝叶斯统计的基本概念，以及积极预测值——这表达了已发表研究结果为真的概率。我们将体验选择性停止的问题，并学习如何通过使用序贯分析来防止这些问题。您将计算效应大小，通过模拟观察置信区间的工作原理，并练习进行先验功效分析。最后，您将学习如何使用等效性检验和贝叶斯统计来检查原假设是否为真，如何进行研究预注册，以及如何在开放科学框架上分享您的数据。 课程所有视频现均有中文字幕，目前已有超过30000名学习者报名参加！ 如果您喜欢本课程，我们推荐您继续学习我新的课程“改善您的统计问题”。 课程大纲：
名称：导论 + 频率统计
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名称：似然性与贝叶斯统计
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名称：多重比较、统计功效、预注册
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名称：效应大小
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名称：置信区间、样本大小合理性、p-curve分析
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名称：科学哲学与理论
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名称：开放科学
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名称：期末考试
描述：本模块包含练习考试和评分考试。两个测验涵盖整个课程的内容。我们建议在您完成所有其他模块后再进行这些考试。
Name:Introduction + Frequentist Statistics
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Name:Likelihoods & Bayesian Statistics
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Name:Multiple Comparisons, Statistical Power, Pre-Registration
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Name:Effect Sizes
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Name:Confidence Intervals, Sample Size Justification, P-Curve analysis
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Name:Philosophy of Science & Theory
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Name:Open Science
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Name:Final Exam
Description:This module contains a practice exam and a graded exam. Both quizzes cover content from the entire course. We recommend making these exams only after you went through all the other modules.
This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. Then, you will learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for your study, for example in order to achieve high statistical power. Subsequently, you will learn how to interpret evidence in the scientific literature given widespread publication bias, for example by learning about p-curve analysis. Finally, we will talk about how to do philosophy of science, theory construction, and cumulative science, including how to perform replication studies, why and how to pre-register your experiment, and how to share your results following Open Science principles. In practical, hands on assignments, you will learn how to simulate t-tests to learn which p-values you can expect, calculate likelihood ratio's and get an introduction the binomial Bayesian statistics, and learn about the positive predictive value which expresses the probability published research findings are true. We will experience the problems with optional stopping and learn how to prevent these problems by using sequential analyses. You will calculate effect sizes, see how confidence intervals work through simulations, and practice doing a-priori power analyses. Finally, you will learn how to examine whether the null hypothesis is true using equivalence testing and Bayesian statistics, and how to pre-register a study, and share your data on the Open Science Framework. All videos now have Chinese subtitles. More than 30.000 learners have enrolled so far! If you enjoyed this course, I can recommend following it up with me new course "Improving Your Statistical Questions"
