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Comentários e feedback de alunos de Improving your statistical inferences da instituição Universidade Tecnológica de Eindhoven

715 classificações
236 avaliações

Sobre o curso

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"...

Melhores avaliações


13 de mai de 2021

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 de jul de 2021

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.

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201 — 225 de 235 Avaliações para o Improving your statistical inferences

por Wenkai S

16 de fev de 2022

Very informative and helpful!

por Pablo B

22 de set de 2017

Enjoyable, useful, necessary.

por Oana S

27 de dez de 2016

Amazing learning experience

por Maheshwar G

6 de jun de 2020

This is really impactful.

por Zahra A

28 de abr de 2017

Extremely useful course!

por Biju S

5 de dez de 2017

Very interesting course

por Alexander P

23 de jul de 2017

Phenomenal course!

por Pedro V

19 de dez de 2020

Very good course!

por Maria A T

16 de jun de 2017

Excellent course.

por martin j k

6 de nov de 2017

















por Françoise G

2 de jan de 2021

Excellent cours

por Prabal P S B

14 de jul de 2021

Amazing Course

por Sarah W

12 de fev de 2020

Thanks Lakens

por Nareg K

30 de nov de 2018

Great course!

por Michiel T

24 de jul de 2018

Great course!

por Jinhao C

24 de jun de 2018

A must-take!

por Edilson S

9 de abr de 2018


por Daniel K

14 de jan de 2019

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.

por Alicia S J

11 de nov de 2018

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!

por José M V S

20 de out de 2020

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.

por Marija A

12 de out de 2018

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.

por Robert C P

21 de jan de 2018

This course is a great complement to other statistics related courses. Instead of spending time on a bunch of formulas, this class is more about best practices and how to (correctly) apply some of the basic statistical methods.

por Matteo M

5 de ago de 2020

Great course to dig a bit deeper into some very useful statistical concept. 4 starts as many of the contents are not "open" as the course preaches (see Microsoft Office documents or GPower).

por Lior Z

10 de out de 2018

Great course! Highly recommended.

One thing to improve - I would like to see more theory behind the different effect sizes (eta-squared/omega squared/etc)

por Ramón G M

23 de abr de 2018

I recovered my faith in statistics with this course.

Makes me alert not to believe every effect I see in the data.

Teaches to do good science.