Extending the Statistical Toolbox @ BSI PhD-Day 2019
This is the repository of Extending the Statistical Toolbox @ BSI PhD-Day 2019.
- The PowerPoint Slides as PPTX and PDF
R-code producing the plots used on the slides
- Additional material (
R-code and ready-to-read .html version) on Bayesian Mixed-Effects Models in
brms. The material has more information than is mentioned on the slides. More extensive slides on this topic (that contain all the topics mentioned in the material) can be found HERE.
Introduction to Bayesian Statistics
- Statistical Rethinking <-- This is a must-read IMO!
- Doing Bayesian Data Analysis
- Bayesian Data Analysis: The Bible of Bayesian Data Analysis
Papers and Tutorials
- How to become a Bayesian in eight easy steps: An annotated reading list
- Toward a principled Bayesian workflow in cognitive science
On the topics mentioned in the presentation
Critique of current implementation of NHST
- False Positive Psychology
- The Statistical Significance Filter Leads to Overoptimistic Expectations of Replicability
- Redefine Statistical Significance
- Abandon Statistical Significance
- Why Psychologists Must Change the Way They Analyze Their Data
- A Practical Solution to the Pervasive Problems with p values
Why Bayes Factors are Better...
- The philosophy of Bayes factors and the quantification of statistical evidence
- Sequential hypothesis testing with Bayes factors: Efficiently testing mean differences
- How Bayes factors change scientific practice
- Four reasons to prefer Bayesian analyses over significance testing
...But why they are Not
Problems with Bayes Factors
- Avoiding Model Selection in Bayesian Social Research
- Dance of the Bayes factors
- Incorporating Bayes factor into my understanding of scientific information and the replication crisis
- If you think p-values are problematic, wait until you understand Bayes Factors
- Bayes Factors bias the estimate when using them in sequential sampling
- The Default Bayesian Test is Prejudiced Against Small Effects
Seeming advantages are often also possible in the Frequentist Framework
- Equivalence Testing for Psychological Research: A Tutorial
- Improving Inferences about Null Effects with Bayes Factors and Equivalence Tests
- Examining Non-Significant Results with Bayes Factors and Equivalence Tests
- Performing high‐powered studies efficiently with sequential analyses
Beyond NHST and Bayes Factors
- How to embrace variation and accept uncertainty
- Statistical Rethinking <-- Again read this!
- Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning
- It’s not so hard to move away from hypothesis testing and toward a Bayesian approach of “embracing variation and accepting uncertainty.”
- Increasing Transparency Through a Multiverse Analysis.
- Moving Towards the Post p < 0.05 Era via the Analysis of Credibility
- Example paper only reporting posteriors