Chevron Left
Volver a Improving your statistical inferences

Opiniones y comentarios de aprendices correspondientes a Improving your statistical inferences por parte de Universidad Técnica de Eindhoven

724 calificaciones
237 reseña

Acerca del 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"...

Principales reseñas


13 de may. 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.


1 de mar. de 2017

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.

Filtrar por:

201 - 225 de 236 revisiones para Improving your statistical inferences

por David S

15 de feb. de 2021

Great content and lab document.

por Mark K

10 de jul. de 2020

This was an exceptional course!

por Wenkai S

16 de feb. de 2022

Very informative and helpful!

por Pablo B

22 de sep. de 2017

Enjoyable, useful, necessary.

por Oana S

27 de dic. 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 dic. de 2017

Very interesting course

por Alexander P

23 de jul. de 2017

Phenomenal course!

por Pedro V

19 de dic. de 2020

Very good course!

por Maria A T

16 de jun. de 2017

Excellent course.

por Martin K

6 de nov. de 2017

















por Françoise G

2 de ene. de 2021

Excellent cours

por Prabal P S B

14 de jul. de 2021

Amazing Course

por Sarah W

12 de feb. 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 ene. 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 oct. 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 oct. 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 ene. 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).