Retour à Improving your statistical inferences

4.9

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136 avis

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.
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Feb 22, 2018

Excellent course with a lot to learn. After 10 years in data analysis it provided me with great new insights and material to further improve my skills and understanding of data analysis

Oct 06, 2017

This is a top-notch course. The ground (especially pitfalls) is very well covered, and useful free tools are engaged (R, G*Power, prof's own spreadsheets for calculating effect size).

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par Yonathan M P

•Jun 08, 2019

Amazing course! Tons of insights and original thinking!

par Pepe V C

•Jun 01, 2019

The explanations from Daniel are awesome... I am understanding p values in a manner I never did before.

par Daniel A L

•May 25, 2019

As an early career scientist, this course helped me get a solid foundation on statistical inferences. After years of accumulating vaguely-organised statistical concepts and procedures, now I am confident I have mastered the basics. Definitely the best course I've had in a long time!

par Shan G

•Jun 25, 2018

This courses uses R

par Andreas K

•Jul 15, 2019

While the course is for researchers, also non-researchers like myself can get a better understanding for methods and pitfalls in science. You need to have prior knowledge of basic statistics and how to perform statistical tests, such as a t-test. I read up on the latter on the Internet, which proved sufficient.

Most examples are from psychology, but the principles are general. In this brief course, very little mathematics is used, but there are other sources for that. The section on r class effect sizes could have used some more work. (Or perhaps I should know more beforehand?) The final exam may ask questions not explicitly covered in the material; I do not recall any mention of Bonferroni correction, but this is perhaps so basic that it is considered a prerequisite.

par Emmanuel k A

•Jun 21, 2019

I started just today and I'm beginning to love the course

par Kevin H

•May 13, 2019

Very good introduction course. An improvement could be to include more high level summaries of each sections. I think it could help students better organize their thoughts.

par Rodney K

•May 10, 2019

Very comprehensive and enjoyable course, highly recommended.

par Nicholas

•Apr 28, 2019

Fantastic course on inference, difference between frequentist and Bayesian concepts like p-values, confidence and credible intervals, and validity.

par Reuben A

•Apr 17, 2019

The best statistics course I have ever taken

par Andrés C M

•Mar 25, 2019

Excellent course. I improved my statistical knowledge and learned more about bayesian inference. Also, I learned something about how to pre-register a research and its benefits of doing so.

par Maureen M

•Mar 21, 2019

The best MOOC in statistis ever!

par Peter K

•Mar 01, 2019

Excellent course. I learned a lot about inference.

par César A Y B

•Feb 26, 2019

Practico sin hacer a un lado lo teorico, te dan un marco mucho mas amplio para la interpretacion y planteamiento de hipotesis

par Bruno V

•Feb 19, 2019

Thank you daniel, very educational, I learned a lot

par Esthelle E

•Jan 23, 2019

It was truly an awesome course! I learned a lot from the very well done videos, and well thought-through assignment. Would recommend to anyone trying to marry theory and application in ways that are actually helpful! BRAVO!

par Richard M

•Jan 22, 2019

Great course. A lot of topics introduced and explored. Well worth the time.

par Daniel K

•Jan 15, 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.

par Romain R

•Jan 10, 2019

Great overview of statistics and philosophy of science. Now I know what to tell my students when they ask me about p-values. At last !

par Leanne C

•Jan 03, 2019

Very informative course, well taught and with lots of useful practice built into the assignments.

par Jason L

•Dec 07, 2018

I really enjoyed the course and found it challenging at times. Its definitely worth the time and effort as my knowledge has improved dramatically. I have gained knowledge which will be really helpful in the future for correctly interpreting current literature as well as future reporting of data and building research ideas. I also appreciate all the effort put into this course and the tools provided which will be beneficial to me in the future. I have saved a lot of the webpages and tools for future reference and will definitely use them when beginning research as well as examining current literature. Excellent

par Dennis H

•Dec 04, 2018

excellent refresher and expansion on frequentists stats (interpretation) and nice intro to bayesian stats. highly recommended.

par Nareg K

•Nov 30, 2018

Great course!

par Bertin

•Nov 17, 2018

This course is amazing, dynamic and entertaining. Daniel Lakens is brilliant.

par Alicia S J

•Nov 11, 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!

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