Recently I was asked to introduce Bayesian inference in three minutes flat. In 10 slides, available at https://osf.io/68y75/, I made the following points: Bayesian inference is “common sense expressed in numbers” (Laplace) We start with at least two rival accounts of the world, aka hypotheses. These hypotheses make predictions, the quality of which determines their change in plausibility: hypotheses that…
read moreThis month I gave a 45-minute presentation “Transparency in Science and Statistics” for the Italian Reproducibility Network. This presentation reflects my recent thinking on the topic. Important themes include “how to use a Ulysses contract to avoid fooling yourself (and others)”, “how to reveal uncertainty that often remains hidden”, “what is model-myopia (and how to avoid it)”, and “can Mertonian…
read moreTLDR: ChatGPT rocks It has been more than two years since doing a post for the “Redefine Statistical Significance” series (https://www.bayesianspectacles.org/redefine-statistical-significance-xix-monkey-business/). In this series, Quentin Gronau and I demonstrated through countless examples that p-values just below .05 (“reject the null”!) should be interpreted with great caution, as such p-values provide –at best– only weak evidence against the null (see Benjamin…
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Coherence Revisited The previous post gave a demonstration of Bayes factor coherence. Specifically, the post considered a test for a binomial parameter , pitting the null hypothesis against the alternative hypothesis (i.e., the uniform distribution from 0 to 1). For fictitious data composed of 5 successes and 5 failures, the Bayes factor equals about 2.71 in favor of . We…
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Image by Doremi/Shutterstock.com TLDR; The Last Dance This post demonstrates how Bayes factors are coherent in the sense that the same result obtains regardless of whether the data are analyzed all at once, in batches, or one at a time. The key point is that this coherence arises because Bayes factors are relatively sensitive to the prior distribution. Ironically, the…
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The Datacolada post “Meaningless Means: The Average Effect of Nudging is d = 0.43” critiques the recent PNAS meta-analysis on nudging and our commentary “No Evidence for Nudging After Adjusting for Publication Bias” (Maier et al., 2022) for pooling studies that are very heterogeneous. The critique, in fact, echoes many Twitter comments which raised the heterogeneity question immediately after our…
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We are delighted to announce that registration is now open for the annual Amsterdam workshop on probabilistic modelling for cognitive science. The tenth instalment of this workshop takes place August 22–26, 2022. Program In this workshop, plenary lectures provide the theoretical background of Bayesian statistics, and practical computer exercises teach participants how to use the popular JAGS and Stan programs…
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The JASP Team is excited to announce that registration for our annual two-day hybrid Amsterdam workshop is now open. The workshop will be happening both on-site and via Zoom and it takes place on August 29–30, 2022. Program In this two-day workshop, plenary lectures provide the theoretical background of Bayesian statistics and Bayesian hypothesis testing; in addition, practical exercises demonstrate…
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