Eindhoven University of Technology (TU/e) is offering free online course on Improving Statistical Inferences. This course aims to help you to draw better statistical inferences from empirical research.
In this course, you will learn how to interpret evidence in the scientific literature given widespread publication bias, for example by learning about p-curve analysis. The course will start on November 13, 2017.
Course At Glance
Length: 7 weeks of study
Effort: 3 hours a week
Subject: Probability and Statistics
Institution: Eindhoven University of Technology (TU/e) and Coursera
Certificate Available: Yes
Session: Course starts on November 13, 2017
Eindhoven University of Technology (TU/e) is a research-driven, design-oriented university of technology with a strong international focus. The university was founded in 1956, and has around 8,500 students and 3,000 staff. TU/e has defined strategic areas focusing on the societal challenges in Energy, Health and Smart Mobility. The Brainport Eindhoven region is one of world’s smartest; it won the title Intelligent Community of the Year 2011.
About This Course
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.
All videos now have Chinese subtitles. More than 10.000 learners have enrolled so far!
Why Take This Course?
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.
This course is aimed at anyone who wants to improve their statistical inferences, either because you are preparing to do empirical research for the first time, or because you were never taught these important statistical concepts in a clear and accessible manner in the past. I didn’t know most of the things you will learn in this course until well after I got my PhD, and I’ve tried to create the course I would have liked to have gotten when I started to do research. You should have some basic knowledge about calculating descriptive statistics, and how to perform t-tests, correlations, and ANOVA’s (If you don’t have this knowledge, try https://www.coursera.org/learn/basic-statistics first). We will use R in many of the assignments, but you don’t need any previous knowledge of R – we will mainly use it as a fancy calculator.
Daniel Lakens, Associate Professor
Department of Human-Technology Interaction
How To Join This Course
- Go to the course website link
- Sign Up At Coursera
- Select a course and Join