返回到 Improving your statistical inferences

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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 30.000 learners have enrolled so far!
If you enjoyed this course, I can recommend following it up with me new course "Improving Your Statistical Questions"...

YK

Mar 1, 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.

PP

Jun 28, 2020

Excellent explanations. Strong examples. Helpful exercises. Highly recommended for anyone who ever has to conduct inferential statistics or read anything that reports a p value or bayes factor.

筛选依据：

创建者 Kathryn S

•Dec 9, 2017

I absolutely loved Prof Lakens' clarity! The effort he put into making the material and the assignments easy to understand is astounding.

创建者 侯静波

•Jun 12, 2018

very good course! The teaching style is good and the assignment in R is very helpful for me to understand the main ideas of this course.

创建者 Mathew L

•Jun 4, 2017

One of the best courses I've ever done. Fundamentally practical. I learned a great deal and challenged a lot of my implicit assumptions.

创建者 Jana H

•Mar 4, 2017

Wonderful course! It was really well-conceived and I learned a lot. Would definitely recommend it to everyone interested in statistics!

创建者 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 !

创建者 Munzar A S

•Apr 10, 2020

Fabulous course! Points out a lot of the nonsense going on in psychological research, how we can spot it, and how we can do better!

创建者 marcus n

•Feb 4, 2017

Great high level overview of intermediate applied statistics. The instructors presentation skills and pace are very good as well.

创建者 Maxim P

•Mar 22, 2020

Such a wonderful course, I really enjoyed the walkthrough. Also, I'd like to note the perfect English language of the lecturer.

创建者 Dennis H

•Dec 4, 2018

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

创建者 Katia D

•Feb 11, 2018

Great course! Although I was struggling with lecture 2 (Bayesian Statistics)––It was very mathsy and a bit difficult to follow.

创建者 Cesar Y

•Feb 25, 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 Bernhard E

•Jan 2, 2021

Great course; didactically and scientifically excellent. I would recommend it to all PhD students in the empirical sciences.

创建者 Agustin E C F

•Nov 5, 2019

This is a great course!. It tackles common misbeliefs and approaches the topics both in a technical and coloquial manner.

创建者 Ezra H

•May 19, 2020

Very well structured. Every week covered a different important topic. Overall a useful course for empirical researchers.

创建者 Maojie T

•Jan 1, 2020

I think it's a useful course for me, but I think some content in the last week is a little bit trivial for me...

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 Davide F S

•May 21, 2017

Clear, concise, and engaging explanation of many statistical concepts that can be readily applied in research.

创建者 Amy M

•Nov 2, 2016

Great lectures and really helpful simulations. Very engaging and interesting. Full of useful resources.

创建者 Lydia A G

•May 28, 2020

Highly recommendable course. It puts clarity from the most basic concepts to some other new insights.

创建者 Sandra V

•Dec 10, 2016

Extremely useful cours, especially the first 5 weeks! Pleasant and enjoyable. Definitely recommended!

创建者 Fengyuan L

•Jul 31, 2020

excellent course. It solves lots of my question over the p value as well as the statistic analysis.

创建者 Habiba A

•Dec 29, 2016

Easy to follow, light workload, and most importantly: very useful material of supreme importance.

创建者 Thijs

•Aug 14, 2019

Great course. Already had some knowledge about statistics, but this course really improved it.

创建者 Mr. J

•Feb 24, 2020

Superbly Done synopsis of statistical gotchas and best practice against them. Very Valauble.

创建者 Morio C

•Jan 2, 2020

Great course, clear and helpful. I will definitely recommend it to colleagues and students.

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