返回到 Improving your statistical inferences

4.9

星

590 个评分

•

187 条评论

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"...

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.

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.

筛选依据：

创建者 Matteo M

•Aug 06, 2020

Great course to dig a bit deeper into some very useful statistical concept. 4 starts as many of the contents are not "open" as the course preaches (see Microsoft Office documents or GPower).

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.