Several researchers have proposed that the capacity for mental control is a limited resource, one that can be temporarily depleted after having engaged in a taxing cognitive activity. This hypothetical phenomenon — called ego depletion — has been hotly debated, and its very existence has been called into question. We ourselves are in the midst of a multi-lab collaborative research…
read more
This Tuesday, one of us [EJ] participated in a debate about –you guessed it– the α = .005 recommendation from the paper ‘Redefine Statistical Significance’. The debate was organized as part of the Annual Meeting of the Berkeley Initiative for Transparency in the Social Sciences (BITSS), and the two other discussants were Simine Vazire and Daniel Lakens. The debate was…
read more
This week, Dorothy Bishop visited Amsterdam to present a fabulous lecture on a topic that has not (yet) received the attention it deserves: “Fallibility in Science: Responsible Ways to Handle Mistakes”. Her slides are available here. As Dorothy presented her series of punch-in-the-gut, spine-tingling examples, I was reminded of a presentation that my Research Master students had given a few…
read more
The paper “Redefine Statistical Significance” continues to make people uncomfortable. This, of course, was exactly the goal: to have researchers realize that a p-just-below-.05 outcome is evidentially weak. This insight can be painful, as many may prefer the statistical blue pill (‘believe whatever you want to believe’) over the statistical red pill (‘stay in Wonderland and see how deep the…
read more
For Christian Robert’s blog post about the bridgesampling package, click here. Bayesian inference is conceptually straightforward: we start with prior uncertainty and then use Bayes’ rule to learn from data and update our beliefs. The result of this learning process is known as posterior uncertainty. Quantities of interest can be parameters (e.g., effect size) within a single statistical model or…
read more
This post is based on the example discussed in Wagenmakers et al. (in press). The Misconception Bayes factors are a measure of absolute goodness-of-fit or absolute pre- dictive performance. The Correction Bayes factors are a measure of relative goodness-of-fit or relative predictive performance. Model A may outpredict model B by a large margin, but this does not imply that model…
read more
This post is inspired by Morey et al. (2016), Rouder and Morey (in press), and Wagenmakers et al. (2016a). The Misconception Bayes factors may be relevant for model selection, but are irrelevant for parameter estimation. The Correction For a continuous parameter, Bayesian estimation involves the computation of an infinite number of Bayes factors against a continuous range of different point-null…
read more
“By seeking and blundering we learn.” – Johann Wolfgang von Goethe Bayesian methods have never been more popular than they are today. In the field of statistics, Bayesian procedures are mainstream, and have been so for at least two decades. Applied fields such as psychology, medicine, economy, and biology are slow to catch up, but in general researchers now view…
read more