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Отзывы учащихся о курсе Improving your statistical inferences от партнера Технический университет Эйндховена

Оценки: 727

О курсе

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"...

Лучшие рецензии


13 мая 2021 г.

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.


10 июля 2021 г.

Solid course which taught me how to interpret p-values in a variety of contexts and taught me to not just to consider but (systematic and practical) ways of how to correct for publication bias.

Фильтр по:

201–225 из 238 отзывов о курсе Improving your statistical inferences

автор: Farid

12 мар. 2017 г.

Exactly what i needed. But now it

автор: Maureen M

20 мар. 2019 г.

The best MOOC in statistis ever!

автор: David S

15 февр. 2021 г.

Great content and lab document.

автор: Mark K

10 июля 2020 г.

This was an exceptional course!

автор: Wenkai S

16 февр. 2022 г.

Very informative and helpful!

автор: Pablo B

22 сент. 2017 г.

Enjoyable, useful, necessary.

автор: Oana S

27 дек. 2016 г.

Amazing learning experience

автор: Maheshwar G

6 июня 2020 г.

This is really impactful.

автор: Zahra A

28 апр. 2017 г.

Extremely useful course!

автор: Biju S

5 дек. 2017 г.

Very interesting course

автор: Alexander P

23 июля 2017 г.

Phenomenal course!

автор: Pedro V

19 дек. 2020 г.

Very good course!

автор: Maria A T

16 июня 2017 г.

Excellent course.

автор: Martin K

6 нояб. 2017 г.

















автор: Françoise G

2 янв. 2021 г.

Excellent cours

автор: Prabal P S B

14 июля 2021 г.

Amazing Course

автор: Sarah W

12 февр. 2020 г.

Thanks Lakens

автор: Nareg K

30 нояб. 2018 г.

Great course!

автор: Michiel T

24 июля 2018 г.

Great course!

автор: Jinhao C

24 июня 2018 г.

A must-take!

автор: Edilson S

9 апр. 2018 г.


автор: Daniel K

14 янв. 2019 г.

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.

автор: Alicia S J

11 нояб. 2018 г.

Good pacing and ratio of exercises/lecture. I found the assignments very useful and the instructions easy to follow. Comparing my performance on the pre-tests and pop quizzes at the beginning of the course to those at the end clearly demonstrates that the coursework honed my stats intuition, and I'm very grateful! The only critical feedback I have is that occasionally, I found the wording of test/quiz questions to be a bit confusing. Thanks!

автор: José M V S

20 окт. 2020 г.

I would like that pdf for assignment be in another languages. Some concepts can be difficult for a beginner, just to improve, not a major issue.

I want to focus on the time indicated to complete this course. In my experience, I took so much time than the estimated. May i dont have a intermediate level, but I think that, at least, it should be take in consideration.

автор: Marija A

12 окт. 2018 г.

I find this course very useful, since these are topics that do not stick when you are completely new to statics, but are very useful once you have few years experience in practice. My only remark is that sometimes the multiple choice answers in the quizzes were not clear enough, so a bit confusing.