# 學生對 埃因霍温科技大学 提供的 Improving your statistical inferences 的評價和反饋

4.9
678 個評分
219 條評論

## 課程概述

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"...

## 熱門審閱

MS
2021年5月13日

Eye opening course. My first introduction to some of the issues surrounding p-values as well as how to better utilize them and what they truly represent. My first introduction to effect sizes as well.

YK
2017年3月1日

Excellent course. The lecturer has written code snippets that let the students visualize the meaning and interrelationship of p-values confidence-intervals power effect-size bayesian-inference.

## 151 - Improving your statistical inferences 的 175 個評論（共 217 個）

2020年12月28日

This was a very informative, interesting, simple and logical course!

2018年8月29日

An excellent, informative, organized course. Highly recommended!

2017年4月10日

Great course! Everybody doing human/social science should do it!

2018年4月30日

The course is excellent. I only wish that I'd enrolled sooner!

2019年5月10日

Very comprehensive and enjoyable course, highly recommended.

2017年12月26日

Very great work to help people to listen this great courses!

2017年1月18日

One of the best Coursera courses! Daniel Lakens for the win!

2020年8月23日

very informative and hands-on approach. thank you Dr Lakens

2020年3月14日

Excellent course to get an overview of pratical statistics.

2017年1月3日

It is good indeed. Such course is needed more on Coursera.

2020年8月24日

Magnific! Best statistics course I've ever seen anywhere.

2020年8月20日

Excellent lecture, every social scientist should take!

2020年4月24日

A must take course for any advance statistics student.

2019年11月8日

Amazing course! Very useful to researchers in any area

2016年12月30日

fun and very informative course - thank you very much!

2018年4月29日

Very engaging, I love the way this course is taught!

2017年6月12日

very, very great course about inferential statistics

2019年2月19日

Thank you daniel, very educational, I learned a lot

2019年3月1日

Excellent course. I learned a lot about inference.

2020年6月12日

Un excelente curso guiado por un muy buen maestro

2020年11月20日

The most useful statical course I took so far.

2019年4月17日

The best statistics course I have ever taken

2016年11月26日

Great course, much appreciated. Thanks a lot

2017年10月21日

Excellent content and delivery throughout.

2020年12月6日

It was just great ! Thank you very much !