Вернуться к Improving your statistical inferences

4.9

Оценки: 446

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Рецензии: 146

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!...

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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автор: Shan G

•Jun 25, 2018

This courses uses R

автор: Yonathan M P

•Jun 08, 2019

Amazing course! Tons of insights and original thinking!

автор: Pepe V C

•Jun 01, 2019

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

автор: 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!

автор: 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.

автор: Richard M

•Jan 22, 2019

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

автор: 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 !

автор: 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

автор: Maureen M

•Mar 21, 2019

The best MOOC in statistis ever!

автор: Peter K

•Mar 01, 2019

Excellent course. I learned a lot about inference.

автор: 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.

автор: Dennis H

•Dec 04, 2018

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

автор: 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

автор: 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!

автор: Bruno V

•Feb 19, 2019

Thank you daniel, very educational, I learned a lot

автор: John B

•Jul 17, 2018

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

автор: Michiel T

•Jul 24, 2018

Great course!

автор: 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).

автор: 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)

автор: 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.

автор: 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.

автор: 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.

автор: Caroline W

•Jun 17, 2017

I thought this was an excellent and enjoyable course. Daniel Simons is a great teacher, and I learned a lot as well as picking up some practical tools for the future, such as easy to use spreadsheets to calculate and convert effect sizes, and confidence intervals. I'm an R novice, but got on fine with it and really appreciated the pedagogical value of the R-simulations.

автор: 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!

автор: Alexander P

•Jul 23, 2017

Phenomenal course!

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