返回到 Improving your statistical inferences

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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"...

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.

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：

创建者 Daniel K

•Jan 15, 2019

Thanks to the creators of this course for putting together an engaging curriculum. One note of criticism is that the assignments for Week 5 required G*power software which as far as I can tell is not available on Linux (I'm running Ubuntu).

The practical examples, specifically the example of the impact of Facebook's A/B testing were particularly interesting. I think this course has improved the tools I have at my disposal for interpreting the language commonly used in academic reporting, and I'm confident the information and tools presented will help in my own research in the coming years.

创建者 Alicia S J

•Nov 11, 2018

Good pacing and ratio of exercises/lecture. I found the assignments very useful and the instructions easy to follow. Comparing my performance on the pre-tests and pop quizzes at the beginning of the course to those at the end clearly demonstrates that the coursework honed my stats intuition, and I'm very grateful! The only critical feedback I have is that occasionally, I found the wording of test/quiz questions to be a bit confusing. Thanks!

创建者 Marija A

•Oct 12, 2018

I find this course very useful, since these are topics that do not stick when you are completely new to statics, but are very useful once you have few years experience in practice. My only remark is that sometimes the multiple choice answers in the quizzes were not clear enough, so a bit confusing.

创建者 Robert C P

•Jan 21, 2018

This course is a great complement to other statistics related courses. Instead of spending time on a bunch of formulas, this class is more about best practices and how to (correctly) apply some of the basic statistical methods.

创建者 Lior Z

•Oct 10, 2018

Great course! Highly recommended.

One thing to improve - I would like to see more theory behind the different effect sizes (eta-squared/omega squared/etc)

创建者 Ramón G M

•Apr 23, 2018

I recovered my faith in statistics with this course.

Makes me alert not to believe every effect I see in the data.

Teaches to do good science.

创建者 Max R

•Nov 29, 2019

It was nice. I initially hoped the course would have made some technical details intuitively graspable, but it was fine as it is.

创建者 Mage I

•Jun 20, 2018

The course was very useful, I enjoyed Daniel's advice. However, I wasn't able to make R work, so I couldn't do the exams.

创建者 Sanne D

•May 27, 2018

Questions are sometimes hard to understand if you are not a native speaker of the English language

创建者 Leanne C

•Jan 03, 2019

Very informative course, well taught and with lots of useful practice built into the assignments.

创建者 ese10

•Jul 29, 2019

Very informative.

创建者 Yao Y

•Nov 27, 2016

The video is ok, but it lacks a lot of details in calculation. The assignment is very confusing because some questions refer to some 'previous' statement while fail to clarify which is related.

创建者 Emmanuel k A

•Jun 21, 2019

I started just today and I'm beginning to love the course

创建者 Dashakol

•Sep 21, 2018

I dropped the course at Lecture 1.2 when it was supposed to really teach me what is p-value but it failed. A 20 min video without telling much about p-value and also adding more confusion and unanswered questions at the end. Like what is p-value distribution?

I expected to receive a decent step by step tutorial on statistics starting from basics but it was just another convoluted stuff on statistics.