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

4.9
estrelas
678 classificações
219 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

MS
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.

YK
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.

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176 — 200 de 217 Avaliações para o Improving your statistical inferences

por MASCIANTONIO

8 de Jun de 2020

This course is very useful! I recommend.

por Rossella M

25 de Mar de 2020

Really useful and interesting course!

por JOHN Q

4 de Jun de 2017

Interesting Course. Thanks so much!

por Eleonora N

17 de Jul de 2020

Just great. Very insightful course.

por Farid

12 de Mar de 2017

Exactly what i needed. But now it

por Maureen M

20 de Mar de 2019

The best MOOC in statistis ever!

por David S

15 de Fev de 2021

Great content and lab document.

por Mark K

10 de Jul de 2020

This was an exceptional course!

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

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por Françoise G

2 de Jan de 2021

Excellent cours

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

Nice!

por Alex G

26 de Out de 2016

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.

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.