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An Introduction to (Bayesian) Statistics

This two hour workshop will be presented at the 31st EFPSA Congress in Qakh, Azerbaijan. There are two versions, a more extensive one, and a more concise one with about 40 slides less. I will present the latter, but you can view the former online here.

Most students dislike statistics not because it's hard, but because it's unintuitive or even confusing. And it's true: classical statistical concepts such as the p-value and confidence intervals are exceptionally difficult to grasp; for students and statisticians alike. However, statistical inference is the universal tool of science, and a good scientist must have a good command of it. Eschewing the standard way of teaching statistics in psychology, i.e., introducing loosely connected tests in a cookbook-oriented fashion, in this workshop, I provide an introduction to statistics from "first principles". I discuss its exciting history, controversies, and enigmatic key players. After introducing probability as the means to quantify uncertainty, I focus on the role of statistical modeling. On a real-life example, I illuminate the problems of parameter estimation, hypothesis testing, and model prediction both from a classical and Bayesian perspective. This allows me to (re)introduce you to concepts such as maximum likelihood, confidence intervals, and p-values, as well as outline a more intuitive and powerful approach to statistics --- the Bayesian approach. In the last, practical segment we use JASP ( to apply Bayesian principles to data sets from real research. All materials including slides, code, and further resources will be made available at Note that there are no prerequisites for this workshop. All that you need to bring is a laptop and a focused mind!


Further resources

  • Papers
    • Bayesian Inference for Psychology. Part I and II (2017) by Wagenmakers et al.
    • Bayesian Benefits for the Pragmatic Researcher (2016) by Wagenmakers, Morey, and Lee
    • How to become a Bayesian in eight easy steps (2016) by Etz, Gronau, Dablander, Edelsbrunner, and Baribault
    • The philosophy of Bayes factors and the quantification of statistical evidence (2016) by Morey, Romeijn, & Rouder
    • Statistical tests, p-values, confidence intervals, and power: a guide to misinterpretations (2016) by Greenland et al.
  • Textbooks
    • Statistical Rethinking (2016) by McElreath
    • Introduction to Probability (2014) by Blitzstein & Hwang
    • Bayesian Cognitive Modeling (2013) by Lee & Wagenmakers
  • Popular Science Books
    • Statistics Done Wrong (2015) by Alex Reinhart
    • Superforecasting (2016) by Tedlock & Gardner
    • The Theory that would not die (2011) by McGrayne
    • The Seven Pillars of Statistical Wisdom (2016) by Stephen Stigler
  • Blogs
  • Further resources


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.


Introductory Workshop to (Bayesian) Statistics






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