返回到 Improving your statistical inferences

4.9

470 个评分

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154 个审阅

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!...

Mar 02, 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.

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

筛选依据：

创建者 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!

创建者 Shan G

•Jun 25, 2018

This courses uses R

创建者 Yonathan M P

•Jun 08, 2019

Amazing course! Tons of insights and original thinking!

创建者 Pepe V C

•Jun 01, 2019

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

创建者 Julien B

•Jul 21, 2019

Amazing course! Many thanks to Daniel Lakens for the time spent on this. It's really useful and I've learned so many things I will use to make better research.

创建者 Richard M

•Jan 22, 2019

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

创建者 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

创建者 Maureen M

•Mar 21, 2019

The best MOOC in statistis ever!

创建者 Peter K

•Mar 01, 2019

Excellent course. I learned a lot about inference.

创建者 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.

创建者 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

创建者 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!

创建者 John B

•Jul 17, 2018

very well organised course and deepens understanding. Excellent resources provided also, e.g. books and papers.

创建者 Michiel T

•Jul 24, 2018

Great course!

创建者 Bob H

•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).

创建者 Emmanuel D

•Apr 10, 2018

A real pleasure to take this course ! The videos are extremely pleasant to watch and give away a lot of knowledge, without ever having this feeling of getting lost ! The assignments are fair and extremely useful as well as the exams ! Will definitely recommend (and actually already have ! =P)

创建者 Sebastian U

•Mar 26, 2018

The course gave me useful insight into interpreting and handling statistical parameters. Information and methods were well balanced. Thank you.

创建者 Tyson W B

•Feb 23, 2018

An excellent course! I've taught undergraduate statistics in psychology and consider myself reasonably well-versed in statistics and this was a very helpful expansion.

The course focuses on concepts rather than equations and R programming. Equations are presented, but the focus is on the concept underlying the equation. This course uses R as the analysis software and I had no prior experience with R, but that was not a problem as the instructions are detailed enough to follow along while focusing attention on the statistical concepts.

创建者 Anna S K

•Mar 22, 2018

Great course with practical examples and exercises! Clearly explains typical statistical misunderstandings and provides tips for a responsible and honest scientific practice. I really enjoyed it and already recommended it to all of my colleagues.

创建者 Benedikt L

•Jun 22, 2018

This course was a great opportunity to reflect my statistical inference knowledge. I hold a master of science in psychology and already learned most of the stuff presented. But the course gave a great overview of the fundamentals of statistical inferences and made me really think twice about how to conduct science properly. I was able to deepen my knowledge and improved my understanding of the statistical fundamentals. I even learned a lot new things that were not covered in the university courses I had! The course is thus not only for beginners, but also for people who already have some knowledge in statistics. Also the course was really enjoyable and had just the right amount of information within each section. All the materials - videos, examples, further readings, exercises and pop-up-quizes varied and were very well designed! The examples were practically relevant (often based on real studies in the literature and not just artificially constructed) and sometimes also really humorous. Thanks a lot to the lecturer for this great opportunity to improve my knowledge!

创建者 Gerald R

•Sep 02, 2017

a very thoughtful introduction to the different approaches of statistical reasoning

创建者 Yaron K

•Mar 02, 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.

创建者 Jonas S

•Nov 16, 2016

Very well designed course, from a didactic as well as from an entertainment point of view. I was able to close many gaps in my inferential statistics knowledge and now feel much more confident in my interpretations.

创建者 martin j k

•Nov 06, 2017

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创建者 Stefan W

•Dec 28, 2016

This course is totally awesome! Statistical inference is critical in any science. Why collect data if we do not know what to infer from the data? Unfortunately, many disciplines use outdated or incorrect practices. This course provides an excellent review of state of the art approaches and provides students with many thought-proving opportunities to practice their inferential skills. As a professor of Psychology, I am not embarrassed to say that I learned lots from this course. The lectures, demos, and R scripts are useful tools that I will integrate in my teaching and my own research. Although the course topic is challenging, the course is organized well and does not drown students in technical terms. However, if you take this course, you better be serious and dedicated. The course is challenging, but the knowledge and skills gained are a rewarding experience.