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…
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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…
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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…
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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…
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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…
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“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 moreI (Alex Etz) recently attended the American Statistical Association’s “Symposium on Statistical Inference” (SSI) in Bethesda Maryland. In this post I will give you a summary of its contents and some of my personal highlights from the SSI. The purpose of the SSI was to follow up on the historic ASA statement on p-values and statistical significance. The ASA…
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Bayesian inference offers the pragmatic researcher a series of perks (Wagenmakers, Morey, & Lee, 2016). For instance, Bayesian hypothesis tests can quantify support in favor of a null hypothesis, and they allow researchers to track evidence as data accumulate (e.g., Rouder, 2014). However, Bayesian inference also confronts researchers with new challenges, for instance concerning the planning of experiments. Within the…
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