Chevron Left
Voltar para Improving your statistical inferences

Comentários e feedback de alunos de Improving your statistical inferences da instituição Universidade Tecnológica de Eindhoven

4.9
433 classificações
144 avaliações

Sobre o curso

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!...

Melhores avaliações

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

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).

Filtrar por:

1 — 25 de {totalReviews} Avaliações para o Improving your statistical inferences

por Shan G

Jun 25, 2018

This courses uses R

por Yonathan M P

Jun 08, 2019

Amazing course! Tons of insights and original thinking!

por Pepe V C

Jun 01, 2019

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

por 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!

por 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.

por Bertin

Nov 17, 2018

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

por Dennis H

Dec 04, 2018

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

por 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

por 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 !

por Richard M

Jan 22, 2019

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

por 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!

por Bruno V

Feb 19, 2019

Thank you daniel, very educational, I learned a lot

por 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

por Peter K

Mar 01, 2019

Excellent course. I learned a lot about inference.

por Nareg K

Nov 30, 2018

Great course!

por Maureen M

Mar 21, 2019

The best MOOC in statistis ever!

por Ernesto M

Jul 30, 2018

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

por Aishwar D

Aug 25, 2018

Thank you Daniel Lakens for creating and sharing this course in the way you have done. The content is very appropriate for any one anyone who is looking to work with Inferential Statistics. Many thanks

por Jose J P N

Oct 09, 2018

A great course to learn or refresh theoretical concepts behind statistical inferences. There is also a lot of hands-on material and additional content. I think I will come back to the videos and slides when I want to refresh some concepts.

por Jan N

Oct 11, 2018

Nicely packed body of information necessary to understand your data and to infer any judgements about real world impact of scientific research. The course led me to question my way of creating inferences about my research and conclusions of others. Now, I can be more precise in formulating hypotheses and interpreting results in the way that is closer to truth. Thank you.

por Yoel S

Sep 16, 2018

One of the best online courses I've ever taken! (completed it just now). Great lectures, great materials, great assignments. Links and information for anyone wanting to go deeper on any topic. Brilliant and engaing lecturer who provides the information with so much passion and interest that it "catches on" to you. I especially liked how actual studies are used as examples for learning/assignments. Bottom line - in my opinion it's a must do course to anyone who is interested in inferential statistics.

por 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).

por 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)

por 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.

por 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.