Currently Browsing: Misconceptions
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Einstein’s Riddle

### Summary

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Cicero and the Greeks on Necessity and Fortune

### Cicero Citatus, Glans Inflatus?

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The Merovingian, or Why Probability Belongs Wholly to the Mind

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The Butler, The Maid, And The Bayes Factor

### The Misconception

### The Correction

### The Explanation

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Bayes Factors for Those Who Hate Bayes Factors

## The Misconception

## The Correction

## The Explanation

Posted on Mar 14th, 2018

Einstein confused his students with a riddle about probability – or was it Einstein himself who was confused?

Albert Einstein disliked the idea that the laws of nature were inherently probabilistic. ‘God does not play dice with the universe,’ he stated famously and repeatedly. Yet, physicists like Niels Bohr strongly advocated the idea –based on the ‘Copenhagen interpretation’ of quantum theory– that chance is an inalienable and inevitable aspect of nature itself.

Posted on Feb 21st, 2018

Cicero eloquently summarized the philosophical position that the universe is deterministic – all events are preordained, either by nature or by divinity. Although “ignorance of causes” may create the illusion of Fortune, in reality there is only Necessity.

The male academic who cites Cicero generally lacks the insight that, instead of imbuing his writing with gravitas, he inevitably conveys the impression of being a pompous dickhead (‘glans inflatus’). Particularly damaging to a writer’s reputation are Cicero quotations that occur at the start of an article; for, as Horace reminds us, “parturiunt montes, nascetur ridiculus mus”. Indeed, the only academics who seem to get away with citing Cicero are those who study Cicero’s work professionally.

Posted on Feb 16th, 2018

**Summary:** When Bayesians speak of probability, they mean plausibility.

The famous Matrix trilogy is set in a dystopic future where most of mankind has been enslaved by a computer network, and the few rebels that remain find themselves on the brink of extinction. Just when the situation seems beyond salvation, a messiah –called Neo– is awakened and proceeds to free humanity from its silicon overlord. Rather than turn the other cheek, Neo’s main purpose seems to be the physical demolition of his digital foes (‘agents’), a task that he engages in with increasing gusto and efficiency. Aside from the jaw-dropping fight scenes, the Matrix movies also contain numerous references to religious themes and philosophical dilemma’s. One particularly prominent theme is the concept of free will and the nature of probability.

Posted on Nov 11th, 2017

*This post is based on the example discussed in Wagenmakers et al. (in press).*

Bayes factors are a measure of *absolute* goodness-of-fit or *absolute* pre-

dictive performance.

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 *A* is good, appropriate, or useful in absolute terms. In fact, model *A* may be absolutely terrible, just less abysmal than model *B*.

Statistical inference rarely deals in absolutes. This is widely recognized: many feel the key objective of statistical modeling is to quantify the uncertainty about parameters of interest through confidence or credible intervals. What is easily forgotten is that there is additional uncertainty, namely that which concerns the choice of the statistical model.

Posted on Nov 3rd, 2017

*This post is inspired by Morey et al. (2016), Rouder and Morey (in press), and Wagenmakers et al. (2016a).*

Bayes factors may be relevant for model selection, but are irrelevant for

parameter estimation.

For a continuous parameter, Bayesian estimation involves the computation of an infinite number of Bayes factors against a continuous range of different point-null hypotheses.

Let *H*_{0} specify a general law, such that, for instance, the parameter *θ* has a fixed value *θ*_{0}. Let *H*_{1} relax the general law and assign *θ* a prior distribution *p*(*θ* | *H*_{1}). After acquiring new data one may update the plausibility for *H*_{1} versus *H*_{0} by applying Bayes’ rule (Wrinch and Jeffreys 1921, p. 387):