# 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
507 个评分
165 条评论

## 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

## 126 - Improving your statistical inferences 的 150 个评论（共 164 个）

Apr 29, 2018

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

Jun 12, 2017

very, very great course about inferential statistics

Feb 19, 2019

Thank you daniel, very educational, I learned a lot

Mar 01, 2019

Excellent course. I learned a lot about inference.

Apr 17, 2019

The best statistics course I have ever taken

Nov 26, 2016

Great course, much appreciated. Thanks a lot

Oct 21, 2017

Excellent content and delivery throughout.

Mar 25, 2020

Really useful and interesting course!

Jun 04, 2017

Interesting Course. Thanks so much!

Mar 12, 2017

Exactly what i needed. But now it

Mar 21, 2019

The best MOOC in statistis ever!

Sep 22, 2017

Enjoyable, useful, necessary.

Dec 27, 2016

Amazing learning experience

Apr 29, 2017

Extremely useful course!

Dec 05, 2017

Very interesting course

Jul 23, 2017

Phenomenal course!

Jun 16, 2017

Excellent course.

Nov 06, 2017

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Feb 13, 2020

Thanks Lakens

Nov 30, 2018

Great course!

Jul 24, 2018

Great course!

Jun 24, 2018

A must-take!

Apr 09, 2018

Nice!

Aug 21, 2017

Dr. Lakens is a very good instructor. He speaks cleary and he is extremaly focused in each subject he's teaching, Unfortunatelly, he keeps making some jargons in somehow he understand frequentist statistics. I'll list some of mistakes:

1. The p-value is a probability computed assuming *the null hypothesis is true*, that the test statistic would take a value as extreme or more extreme than that actually observed. When he cite "assuming null effect", he merge "effect size" and "NHSTs". This becomes even worst when we use NHST to analyze variable distributions where, by default, we don't have an "effect", but an "assumption". This is valid for all normality test, such anderson-darling or kolgomorov-smirnoff.

2. Furthermore considering the way he decided to approach to null hypothesis, any statistician knows that a null is always wrong and it is the why we dont accept the null. During all the time, in his videos, he insists to use "accepting the null". When he does that, is like a broken guitar in a symphony. It disturbs the video.

3. The control of type II error always involves some sample-size calculations wether we want to acchieve, at minimium, 80% of power. He simply attached a R script to run and he didnt't mention how we can verify if some study has an effect or not. Point and clicking button, in my opinion, is not adequate when we are in a statistical class where the goal is to improve our inferencial skills.

4. Some of quizzes and evaluations have items where options are not presented in a properly way. The subject of each response vary substantly.

I trully hope this feedback will be read in an academic way, which was the intention.

Oct 26, 2016

To get this out of the way: The one star deduction is not related to the content of the course, only to the fact that there is occasional imprecise language and some parts of the material have typos and grammatical slip-ups that show that the course has room for some tightening up.

That being said, the selection of topics that are covered is great. You get a small but full package of both knowledge and tools that'll help you to significantly (no pun intended) improve your research. Not only are statistical pitfalls covered and solutions offered, you also learn something about how to approach your research with the right mind-set in order to produce solid empirical knowledge that contributes to a cumulative science.

I was particularly impressed by how the instructor manages to pack lots of important topics and concepts into his 10 or 15 minutes lectures without it becoming overwhelming. The key to this is his ability to maintain focus and his generally clear and concise language. The course material, too, reflects the ability to present just the right amount of information - not too little, not too much.

Overall, the course feels very pragmatic and hands-on. It proves that good and fruitful science is doable and that you can start right now. It makes you *want* to start right now.