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Improving your statistical inferences(으)로 돌아가기

아이트호벤 공과 대학의 Improving your statistical inferences 학습자 리뷰 및 피드백

713개의 평가
236개의 리뷰

강좌 소개

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"...

최상위 리뷰


2021년 5월 13일

Eye opening course. My first introduction to some of the issues surrounding p-values as well as how to better utilize them and what they truly represent. My first introduction to effect sizes as well.


2021년 7월 10일

Solid course which taught me how to interpret p-values in a variety of contexts and taught me to not just to consider but (systematic and practical) ways of how to correct for publication bias.

필터링 기준:

Improving your statistical inferences의 235개 리뷰 중 201~225

교육 기관: Wenkai S

2022년 2월 16일

Very informative and helpful!

교육 기관: Pablo B

2017년 9월 22일

Enjoyable, useful, necessary.

교육 기관: Oana S

2016년 12월 27일

Amazing learning experience

교육 기관: Maheshwar G

2020년 6월 6일

This is really impactful.

교육 기관: Zahra A

2017년 4월 28일

Extremely useful course!

교육 기관: Biju S

2017년 12월 5일

Very interesting course

교육 기관: Alexander P

2017년 7월 23일

Phenomenal course!

교육 기관: Pedro V

2020년 12월 19일

Very good course!

교육 기관: Maria A T

2017년 6월 16일

Excellent course.

교육 기관: martin j k

2017년 11월 6일

















교육 기관: Françoise G

2021년 1월 2일

Excellent cours

교육 기관: Prabal P S B

2021년 7월 14일

Amazing Course

교육 기관: Sarah W

2020년 2월 12일

Thanks Lakens

교육 기관: Nareg K

2018년 11월 30일

Great course!

교육 기관: Michiel T

2018년 7월 24일

Great course!

교육 기관: Jinhao C

2018년 6월 24일

A must-take!

교육 기관: Edilson S

2018년 4월 9일


교육 기관: Daniel K

2019년 1월 14일

Thanks to the creators of this course for putting together an engaging curriculum. One note of criticism is that the assignments for Week 5 required G*power software which as far as I can tell is not available on Linux (I'm running Ubuntu).

The practical examples, specifically the example of the impact of Facebook's A/B testing were particularly interesting. I think this course has improved the tools I have at my disposal for interpreting the language commonly used in academic reporting, and I'm confident the information and tools presented will help in my own research in the coming years.

교육 기관: Alicia S J

2018년 11월 11일

Good pacing and ratio of exercises/lecture. I found the assignments very useful and the instructions easy to follow. Comparing my performance on the pre-tests and pop quizzes at the beginning of the course to those at the end clearly demonstrates that the coursework honed my stats intuition, and I'm very grateful! The only critical feedback I have is that occasionally, I found the wording of test/quiz questions to be a bit confusing. Thanks!

교육 기관: José M V S

2020년 10월 20일

I would like that pdf for assignment be in another languages. Some concepts can be difficult for a beginner, just to improve, not a major issue.

I want to focus on the time indicated to complete this course. In my experience, I took so much time than the estimated. May i dont have a intermediate level, but I think that, at least, it should be take in consideration.

교육 기관: Marija A

2018년 10월 12일

I find this course very useful, since these are topics that do not stick when you are completely new to statics, but are very useful once you have few years experience in practice. My only remark is that sometimes the multiple choice answers in the quizzes were not clear enough, so a bit confusing.

교육 기관: Robert C P

2018년 1월 21일

This course is a great complement to other statistics related courses. Instead of spending time on a bunch of formulas, this class is more about best practices and how to (correctly) apply some of the basic statistical methods.

교육 기관: Matteo M

2020년 8월 5일

Great course to dig a bit deeper into some very useful statistical concept. 4 starts as many of the contents are not "open" as the course preaches (see Microsoft Office documents or GPower).

교육 기관: Lior Z

2018년 10월 10일

Great course! Highly recommended.

One thing to improve - I would like to see more theory behind the different effect sizes (eta-squared/omega squared/etc)

교육 기관: Ramón G M

2018년 4월 23일

I recovered my faith in statistics with this course.

Makes me alert not to believe every effect I see in the data.

Teaches to do good science.