Improving your statistical inferences

( 147 Reviews )

Created by: Daniel Lakens

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Overall Score : 98 / 100

Course Description

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!

Time: 22h

Instructor Details

Daniel Lakens

4.9 Rating
147 Reviews

Daniel Lakens is an Associate Professor in the Human-Technology interaction group at Eindhoven University of Technology (TU/e). His areas of expertise include meta-science, research methods and applied statistics. Daniel's main lines of empirical research focus on conceptual thought, similarity, and meaning. He also focuses on how to design and interpret studies, applied (meta)-statistics, and reward structures in science.-A large part of his work deals with developing methods for critically reviewing and optimally structuring studies.

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Reviews

4.9

147 total reviews

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By Vyju I on 10-May-19

Very comprehensive and enjoyable course, highly recommended.

By Latonia D on 30-Jul-18

Excellent course that changed my views on interpreting p-values, confidence intervals, etc. and will surely make my statistical inferences much better.

By Paulius U on 17-Jul-17

Great course, lots of new tools and materials that really helped me in my study.

By Thangaraja R on 5-Feb-18

I found this course very well-structured and easily accessible and understandable even to students, while being highly profound and covering most important and and recent pressing topics in methodology and statistics.

By Nicola Z on 23-Jan-17

Probably the best stats course I've ever taken (and also the most fun and enlightening)!

By Matthew B on 7-Jan-18

Probably the most useful course I have ever taken. I think this is essential for anyone who does science. It provides a clear understanding of inferential statistics while discussing common pitfalls and myths surrounding p-values and confidence intervals. Assignments were very useful. Highly recommended!

By Enrique O G on 3-Jan-17

It is good indeed. Such course is needed more on Coursera.

By Vaibhav C on 20-Sep-17

Concepts are explained in an easy-to-understand way with a good use of analogies. Homework assignments are straightforward and useful. I like the way he teaches using simulations. He encourages students to play around with his simulations to discover how changes in the simulations' inputs affect the results.

By Bonface M on 22-Sep-17