Chevron Left
Вернуться к Improving your statistical inferences

Отзывы учащихся о курсе Improving your statistical inferences от партнера Технический университет Эйндховена

4.9
звезд
Оценки: 725

О курсе

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"...

Лучшие рецензии

MS

13 мая 2021 г.

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.

PP

28 июня 2020 г.

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.

Фильтр по:

126–150 из 236 отзывов о курсе Improving your statistical inferences

автор: Maxim P

22 мар. 2020 г.

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

автор: Dennis H

4 дек. 2018 г.

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

автор: Katia D

11 февр. 2018 г.

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

автор: Ester N

24 янв. 2022 г.

L​earned a lot during this course and it left me with a lot of challenges with planning my own research. I am very grateful.

автор: Cesar Y

25 февр. 2019 г.

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

автор: Bernhard E

2 янв. 2021 г.

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

автор: Agustin E C F

5 нояб. 2019 г.

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

автор: Ezra H

19 мая 2020 г.

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

автор: Maojie T

1 янв. 2020 г.

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...

автор: John B

17 июля 2018 г.

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

автор: Davide F S

21 мая 2017 г.

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

автор: Yeison F V F

12 дек. 2021 г.

A perfect course to keep learning and to clarify the doubts about the essential of statistical inference

автор: Amy M

2 нояб. 2016 г.

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

автор: Lydia A G

28 мая 2020 г.

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

автор: Sandra V

10 дек. 2016 г.

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

автор: Fengyuan L

31 июля 2020 г.

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

автор: Habiba A

29 дек. 2016 г.

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

автор: Thijs

14 авг. 2019 г.

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

автор: Rahul P S

21 сент. 2021 г.

The instructor has explained fine details in statistical inferences. Very informative course.

автор: Cezar D S d S

28 мая 2021 г.

Awesome course! I've had amost given up on learning this topic. Thanks for renewing my faith

автор: Mr. J

24 февр. 2020 г.

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

автор: Morio C

2 янв. 2020 г.

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

автор: Jose M S

17 июня 2017 г.

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

автор: Tamires M

30 нояб. 2020 г.

I never thought I would say that about statistics, but: It was fun! Thank you Dr. Lakens!

автор: Patrick H

10 авг. 2020 г.

This course should be taken by any psychologist (and actually anyone who does statistics)