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学生对 埃因霍温科技大学 提供的 Improving your statistical inferences 的评价和反馈

4.9
470 个评分
154 个审阅

课程概述

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 10.000 learners have enrolled so far!...

热门审阅

YK

Mar 02, 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.

MR

Feb 22, 2018

Excellent course with a lot to learn. After 10 years in data analysis it provided me with great new insights and material to further improve my skills and understanding of data analysis

筛选依据:

26 - Improving your statistical inferences 的 50 个评论(共 153 个)

创建者 Tiago C Z

Jun 19, 2018

This course changed my concepts not only about statistics but about research and science. Daniel Lakens is a fantastic lecturer and scientist. I can't recommend this course enough.

创建者 Justyna J Z

Apr 29, 2018

Very engaging, I love the way this course is taught!

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 Brendan P

Oct 21, 2017

Excellent content and delivery throughout.

创建者 Pablo B

Sep 22, 2017

Enjoyable, useful, necessary.

创建者 Michael E

Jun 25, 2017

Thank you. This course represents a great deal of important work for me to continue to revisit and incorporate in my efforts moving forward.

创建者 Jesús D Z M

Jun 12, 2017

very, very great course about inferential statistics

创建者 Muhammad T S

Nov 09, 2017

This is a very powerful course. Simple content but with lots of depth and newer perspective on statistical testing. Learned a lot. Highly recommended.

创建者 Sandra V

Dec 10, 2016

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

创建者 Rikki L

Apr 30, 2018

The course is excellent. I only wish that I'd enrolled sooner!

创建者 Syarif M

Dec 03, 2016

highly recomended for all level. The explanation is very beginners friendly.

创建者 Jayadev H

May 11, 2018

Sooo good! Cant even begin to explain how essential and wonderful this understanding is!

Great thanks to Dr Daniel! Such an expert in the field!

Thank you Dr!

创建者 Zak R

Aug 11, 2017

A brilliantly informative and engaging exploration of some the issues involved in data analysis and hypothesis testing. Though I'm probably still a while away from using many of the techniques covered myself in formal research, I certainly feel better equipped to interpret existing research and spot potential statistical slip-ups. Much recommended!

创建者 Heidi M

Dec 30, 2016

fun and very informative course - thank you very much!

创建者 Mesionis I

Jun 06, 2017

Excellent course!!!!!!Really descriptive with great examples and practises!!

创建者 Hendrik B

Nov 18, 2017

One of the best courses I have done so far on Coursera. Fairly advanced and very helpful for (under-) grad students running experiments or working with data in general.

创建者 Bartek

Oct 30, 2016

This course really delivers on its core premise: it helped me understand the core principles behind frequentist statistics, gave me some basic understanding of Bayesian statistics, and will definitely prevent me from chasing my tail as far as potential future research is concerned.

Although the course seems to be addressed to current and future researchers, I would recommend taking it to anyone interested in science as it will give you tools to read and understand research papers (esp. the basic reports in social science/experimental psychology).

I would consider this course an excellent resource and introduction to the so-called "new statistics", and covers topics crucial to conducting reproducible research.

The lectures are wonderfully taught and explain everything clearly. The hands-on assignments are challenging for the right reason: they test your knowledge and comprehension of the material at hand (some on-line courses did a number on me, and required extracurricular knowledge in order to succeed with completing the assignments).

I think that even stats newbies might be able to take the course and learn a lot, as most of the material pretty much addresses the basic, core philosophy of statistics, and you don't need to know how to conduct specific tests in order to understand what Daniel is trying to share with you.

This course is *the* course for anyone eager to understand what their stats 101 classes failed to even address.

创建者 Oana S

Dec 27, 2016

Amazing learning experience

创建者 Sean H

Nov 27, 2016

I'm so glad I took this class! I learned how to better design experiments and interpret common statistical practices in the literature. The lectures are entertaining and informative, and the professor is charming and funny. Even though I'm an immunologist and the course is aimed at the social sciences, I feel like a better scientist now.

创建者 Wilte Z

Oct 23, 2016

Clear explanations of the concepts of statistics, without too much emphasis on the formulas. With handy references to online tools, like power calculators.

创建者 Matti H

Dec 13, 2016

I encourage all my friends in research to not do anything before doing this course! The pedagogical touch is different to any stats classes I've been on or stats MOOCs I've taken. After many lectures, I was just left staring at the screen, with the phrase "I must tell everyone" repeating in my head :)

创建者 Ramiro B

Nov 06, 2017

I really like this class, it was very useful and the content was high quality. My only issue - which might have nothing to do with the class or the instructor - was that the exams were really long and boring. It would have been more enjoyable to be to have shorter, more focused examinations instead of a long exam at the end of a section or at the end of the class. EdX does this better.

创建者 Oliver C

Dec 17, 2017

A really important course for anyone who wishes to make statistical inferences as part of their research. I highly recommend this for people at all stages in their career - particularly for people currently planning their research. It is very well delivered and will make you question your statistical knowledge.

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 Kathryn S

Dec 10, 2017

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