Chevron Left
返回到 Improving your statistical inferences

學生對 埃因霍温科技大学 提供的 Improving your statistical inferences 的評價和反饋

4.9
627 個評分
205 條評論

課程概述

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"...

熱門審閱

PP
2020年6月28日

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.

YK
2017年3月1日

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.

篩選依據:

101 - Improving your statistical inferences 的 125 個評論(共 203 個)

創建者 Kathryn S

2017年12月9日

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

創建者 侯静波

2018年6月12日

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.

創建者 Mathew L

2017年6月4日

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

創建者 Jana H

2017年3月4日

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

創建者 Romain R

2019年1月10日

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 !

創建者 Munzar A S

2020年4月10日

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!

創建者 marcus n

2017年2月4日

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

創建者 Maxim P

2020年3月22日

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

創建者 Dennis H

2018年12月4日

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

創建者 Katia D

2018年2月11日

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

創建者 Cesar Y

2019年2月25日

Practico sin hacer a un lado lo teorico, te dan un marco mucho mas amplio para la interpretacion y planteamiento de hipotesis

創建者 Agustin E C F

2019年11月5日

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

創建者 Ezra H

2020年5月19日

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

創建者 Maojie T

2020年1月1日

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...

創建者 John B

2018年7月17日

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

創建者 Davide F S

2017年5月21日

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

創建者 Amy M

2016年11月2日

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

創建者 Lydia A G

2020年5月28日

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

創建者 Sandra V

2016年12月10日

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

創建者 Fengyuan L

2020年7月31日

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

創建者 Habiba A

2016年12月29日

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

創建者 Thijs

2019年8月14日

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

創建者 Mr. J

2020年2月24日

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

創建者 Morio C

2020年1月2日

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

創建者 Jose M S

2017年6月17日

Quite interesting and well structured. The contents of this course deserve a wide audience.