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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.

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176 - Improving your statistical inferences 的 200 個評論(共 217 個)

創建者 MASCIANTONIO

2020年6月8日

This course is very useful! I recommend.

創建者 Rossella M

2020年3月25日

Really useful and interesting course!

創建者 JOHN Q

2017年6月4日

Interesting Course. Thanks so much!

創建者 Eleonora N

2020年7月17日

Just great. Very insightful course.

創建者 Farid

2017年3月12日

Exactly what i needed. But now it

創建者 Maureen M

2019年3月20日

The best MOOC in statistis ever!

創建者 David S

2021年2月15日

Great content and lab document.

創建者 Mark K

2020年7月10日

This was an exceptional course!

創建者 Pablo B

2017年9月22日

Enjoyable, useful, necessary.

創建者 Oana S

2016年12月27日

Amazing learning experience

創建者 Maheshwar G

2020年6月6日

This is really impactful.

創建者 Zahra A

2017年4月28日

Extremely useful course!

創建者 Biju S

2017年12月5日

Very interesting course

創建者 Alexander P

2017年7月23日

Phenomenal course!

創建者 Pedro V

2020年12月19日

Very good course!

創建者 Maria A T

2017年6月16日

Excellent course.

創建者 martin j k

2017年11月6日

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創建者 Françoise G

2021年1月2日

Excellent cours

創建者 Sarah W

2020年2月12日

Thanks Lakens

創建者 Nareg K

2018年11月30日

Great course!

創建者 Michiel T

2018年7月24日

Great course!

創建者 Jinhao C

2018年6月24日

A must-take!

創建者 Edilson S

2018年4月9日

Nice!

創建者 Alex G

2016年10月26日

To get this out of the way: The one star deduction is not related to the content of the course, only to the fact that there is occasional imprecise language and some parts of the material have typos and grammatical slip-ups that show that the course has room for some tightening up.

That being said, the selection of topics that are covered is great. You get a small but full package of both knowledge and tools that'll help you to significantly (no pun intended) improve your research. Not only are statistical pitfalls covered and solutions offered, you also learn something about how to approach your research with the right mind-set in order to produce solid empirical knowledge that contributes to a cumulative science.

I was particularly impressed by how the instructor manages to pack lots of important topics and concepts into his 10 or 15 minutes lectures without it becoming overwhelming. The key to this is his ability to maintain focus and his generally clear and concise language. The course material, too, reflects the ability to present just the right amount of information - not too little, not too much.

Overall, the course feels very pragmatic and hands-on. It proves that good and fruitful science is doable and that you can start right now. It makes you *want* to start right now.

創建者 Daniel K

2019年1月14日

Thanks to the creators of this course for putting together an engaging curriculum. One note of criticism is that the assignments for Week 5 required G*power software which as far as I can tell is not available on Linux (I'm running Ubuntu).

The practical examples, specifically the example of the impact of Facebook's A/B testing were particularly interesting. I think this course has improved the tools I have at my disposal for interpreting the language commonly used in academic reporting, and I'm confident the information and tools presented will help in my own research in the coming years.