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: 700
Рецензии: 232

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.

VM
10 июля 2021 г.

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.

Фильтр по:

126–150 из 230 отзывов о курсе Improving your statistical inferences

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)

автор: Eva D P

23 янв. 2017 г.

Probably the best stats course I've ever taken (and also the most fun and enlightening)!

автор: Carlo D V

21 авг. 2020 г.

This course was very useful. I recommend it to anyone who wants to deepen these topics.

автор: Sergey L

1 янв. 2020 г.

The course is full of useful insights and practices. I can definitely recommend it!

автор: Gerald R

2 сент. 2017 г.

a very thoughtful introduction to the different approaches of statistical reasoning

автор: Aviv E

17 июля 2017 г.

Great course, lots of new tools and materials that really helped me in my study.