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. 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.
Who is this class for: 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.
Taught by: Daniel Lakens, Associate Professor
Department of Human-Technology Interaction
| Level | Intermediate |
| Commitment | 7 weeks of study, 3 hours a week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
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Great course for getting to know heterodox statistical paradigms, how open science could improve the scientific endeavor as a whole, the reasons that led to the replication crisis in some scientific areas and how to correct them. Due to this course I feel more confident analyzing scientific papers, meta-analysis and study designs. It also gave me great tools for conducting my own research, like getting to know the TIER protocol and the pre-registration process. This course awakened my interest in philosophy of science to a degree that I will start a second master´s degree in history and philosophy of science next semester. Prof. Lakens is excellent at making complex issues simple to understand, his videos are entertaining, informative and very well thought out.
Great course! Goes over proper statistical inference and its interpretation from multiple perspectives. The hands-on R exercises are invaluable. Don't be scared off by them - you don't really need to know R to do them. If you interpret literature from the psychological or medical fields, this is a great resource.
Extremely useful course!
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DS
Clear, concise, and engaging explanation of many statistical concepts that can be readily applied in research.