Improving your statistical inferences に戻る

4.9

405件の評価

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135件のレビュー

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 10.000 learners have enrolled so far!...

by MR

•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

by BH

•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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134件のレビュー

by Kevin Hamon

•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.

by Rodney Kennedy

•May 10, 2019

Very comprehensive and enjoyable course, highly recommended.

by Nicholas

•Apr 28, 2019

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

by Reuben Addison

•Apr 17, 2019

The best statistics course I have ever taken

by Andrés Campos Mercado

•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.

by Maureen Montanía

•Mar 21, 2019

The best MOOC in statistis ever!

by Peter Kamerman

•Mar 01, 2019

Excellent course. I learned a lot about inference.

by César Alejandro Yépez Barrios

•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

by Bruno Verschuere

•Feb 19, 2019

Thank you daniel, very educational, I learned a lot

by Esthelle Ewusi-Boisvert

•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!