What Determines the Price of LEGO Sets? A Bayesian Analysis with Twists and Turns

Recently we completed a multi-year Odyssey in order to obtain Bayes factors for partial correlations. The result is now available as a preprint. Instead of delving into the finer details of Bayes factors and posterior distributions, we will provide a concrete demonstration. Specifically, we will use Bayes factors for partial correlations to address a perennial question that has tormented children…

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Visualizing the Equation for the Sample Correlation Coefficient

TL;DR We just preprinted a manuscript on how to teach the correlation coefficient with rectangles and squares. Check out the cool pictures. Suggestions for improvement are welcome. Abstract The equation for the Pearson correlation coefficient can be represented in a scatter plot as the difference in area between concordant and discordant rectangles, scaled by an area that represents the maximum possible…

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Rosenkrantz on Severity and the Problem of Old Evidence

In the previous post I discussed the problem of old evidence and wrote “It is highly likely that my argument is old, or even beside the point. I am not an expert on this particular problem.” Sure enough, Andrew Fowlie kindly attended me to the following book chapter by Roger Rosenkrantz: Rosenkrantz, R. D. (1983). Why Glymour is a Bayesian. In…

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The Problem of Old Evidence

  To my shame and regret, I only recently found the opportunity to read the book “Bayesian philosophy of science” (BPS), by Jan Sprenger and Stephan Hartmann. It turned out to be a wonderful book, both in appearance, typesetting, and in contents. The book confirmed many of my prior beliefs ;- ) but it also made me think about the…

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Bayesian Inference in Three Minutes

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…

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A Presentation on Transparency in Science and Statistics

This 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…

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Redefine Statistical Significance XX: A Chat on P-values with ChatGPT

TLDR: 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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Bayes Factors for Those who Hate Bayes Factors, Part III: The Coherence Plot

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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