# 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
684 个评分
223 条评论

## 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 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"...

## 热门审阅

MS
May 13, 2021

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
Jul 10, 2021

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.

## 51 - Improving your statistical inferences 的 75 个评论（共 221 个）

Jun 25, 2018

An intermediate course, which will grant new knowledge to everyone who is interested in making better inferences. It also needs a great deal of studying from external sources for all those who encounter these topics i.e. Type I error inflation, for the first time!

Aug 16, 2018

Taking this course was the best decision of the start of my grad school. It has massively improved my ability to interpret other papers and plan my own experiments, as well as changing how I view psychology/science in general. Plus Daniel is a great teacher :)

Aug 16, 2017

Amazing course. Definitely worth to accomplish. Highly recommended for every researcher, lecturer, PhD. student or student that is interested in prestent state of art regarding choosen important topics statistics and methodology, especially in Psychology.

Mar 22, 2018

Great course with practical examples and exercises! Clearly explains typical statistical misunderstandings and provides tips for a responsible and honest scientific practice. I really enjoyed it and already recommended it to all of my colleagues.

Sep 7, 2019

This course was fantastic. I believe I learned more in this class than I learned in three formal behavioral statistics courses. I highly recommend this course to other grad students, and I look forward to the next course that Lakens is creating!

Oct 9, 2018

A great course to learn or refresh theoretical concepts behind statistical inferences. There is also a lot of hands-on material and additional content. I think I will come back to the videos and slides when I want to refresh some concepts.

Jul 17, 2017

This is an excellent course for firming up statistical knowledge and replicable research practices. Likely useful for all psych/cognitive science PhD students and researchers further along who come from the frequentist training tradition.

Oct 6, 2020

Very good course, with a lot of practical work, which is nice. Also very clear lectures explaining the topics and not too difficult but definitely not too easy exams! Overall fantastic course, which provided me interesting new insights.

May 21, 2021

Even after taking several undergraduate and graduate statistics courses, this class was a great refresher and taught me knew information! I thoroughly enjoyed it and will implement what I learned to improve my research practices.

Jan 5, 2017

This was a really well presented course, giving a fantastic overview of inferential statistics and always presented with a sense of humour! A number of really useful tools where introduced which I will be using again and again.

Jun 21, 2018

Really great course! Was already familiar in statistics, but learned a lot about making inferences based on statistical tests. Lectures and assignments are very clear. Would recommend it to everyone interested in statistics.

Jan 23, 2019

It was truly an awesome course! I learned a lot from the very well done videos, and well thought-through assignment. Would recommend to anyone trying to marry theory and application in ways that are actually helpful! BRAVO!

Jun 10, 2020

Such a great course. Daniel Lakens does a fantastic job explaining the nuances of statistical repeatability with well thought out examples and helpful tools. This is hands down of the best Coursera courses I've completed.

May 18, 2021

The course helps you interpret statistical results and gives a foundation for making better studies. The course did a good job of making analysis first and doesn’t require a student to have strong programming skills.

Nov 28, 2019

This course will actually improve your statistical inferences. It's helpful to get an overview and better understanding of different statistical approaches and a nice introduction into Baysian stats. Would do it again!

Oct 17, 2019

The course is well-structured and excellently taught. The content is well researched and presented. The assignments are very practical and educative. (The philosophical references in the course content were on point!)

Feb 5, 2018

I found this course very well-structured and easily accessible and understandable even to students, while being highly profound and covering most important and and recent pressing topics in methodology and statistics.

Oct 9, 2017

A course taught by a single individual - that packs more learning and knowledge into it than many rote courses. A course that I have returned to and will return to many times in the future to brush up on fundamentals.

Nov 16, 2016

Very well designed course, from a didactic as well as from an entertainment point of view. I was able to close many gaps in my inferential statistics knowledge and now feel much more confident in my interpretations.

Jul 17, 2017

An accessible and interesting course. I learned so much (and refreshed myself on things I should already know!). Thank you so much Dr Lakens for putting together this course. I've been recommending it to everyone!

Jul 18, 2017

It's a really interesing course about statistical inferences. You can learn a lot about how to recollect data, how to analyse it and how to interpret it. It is very recommendable for all kind of researchers.

May 9, 2021

This is an excellent course, one which I’d recommend to anybody with an interest in science, or open science, whether you be a scientist or just someone with an interest. Daniel does an excellent job here.

May 14, 2021

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.

Aug 25, 2018

Thank you Daniel Lakens for creating and sharing this course in the way you have done. The content is very appropriate for any one anyone who is looking to work with Inferential Statistics. Many thanks

Jun 29, 2020

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.