Archive for cartoon

cartoon satire for dummies [from Charlie]

Posted in Books, pictures with tags , , , , , , , , on January 7, 2018 by xi'an

On the very day 12 persons were killed at the Charlie Hebdo offices in 2013, I found it worth reposting a cartoon (en anglais!) from Luz attempting to explain why political satire has to go “too far” to expose dysfunctions in societies, invasions of creeds and the irrational, hypocritical double-talk from governments, and whatever other ideas it aims at criticising. 

absolutely no Bayesians inside!

Posted in Statistics with tags , , , , , , , , on December 11, 2017 by xi'an

5 ways to fix statistics?!

Posted in Books, Kids, pictures, Statistics, University life with tags , , , , , , , on December 4, 2017 by xi'an

In the last issue of Nature (Nov 30), the comment section contains a series of opinions on the reproducibility crisis, by five [groups of] statisticians. Including Blakeley McShane and Andrew Gelman with whom [and others] I wrote a response to the seventy author manifesto. The collection of comments is introduced with the curious sentence

“The problem is not our maths, but ourselves.”

Which I find problematic as (a) the problem is never with the maths, but possibly with the stats!, and (b) the problem stands in inadequate assumptions on the validity of “the” statistical model and on ignoring the resulting epistemic uncertainty. Jeff Leek‘s suggestion to improve the interface with users seems to come short on that level, while David Colquhoun‘s Bayesian balance between p-values and false-positive only address well-specified models. Michèle Nuitjen strikes closer to my perspective by arguing that rigorous rules are unlikely to help, due to the plethora of possible post-data modellings. And Steven Goodman’s putting the blame on the lack of statistical training of scientists (who “only want enough knowledge to run the statistical software that allows them to get their paper out quickly”) is wishful thinking: every scientific study [i.e., the overwhelming majority] involving data cannot involve a statistical expert and every paper involving data analysis cannot be reviewed by a statistical expert. I thus cannot but repeat the conclusion of Blakeley and Andrew:

“A crucial step is to move beyond the alchemy of binary statements about ‘an effect’ or ‘no effect’ with only a P value dividing them. Instead, researchers must accept uncertainty and embrace variation under different circumstances.”

do cartoons help?

Posted in Books, Kids, Running, University life with tags , , , , , , on November 8, 2015 by xi'an

I received a (mass) email from Taylor & Francis about creating a few cartoons related to recent papers… As in the example above about the foot strike of Kilian Jornet. With a typo on Font-Romeu. Apart from the authors themselves, and maybe some close relatives!, I have trouble seeing the point of this offer, as cartoons are unlikely to attract academic readers interested in the contents of the paper.

the cartoon introduction to statistics

Posted in Books, Kids, Statistics, University life with tags , , , , , on May 16, 2013 by xi'an

A few weeks ago, I received a copy of The Cartoon Introduction to Statistics by Grady Klein and Alan Dabney, send by their publisher, Farrar, Staus and Giroux from New York City.  (Never heard of this publisher previously, but I must admit the aggregation of those three names sounds great!) As this was an unpublished version of the book, to appear in July 2013, I first assumed my copy was a draft version, with black and white drawings using limited precision graphics.. However, when checking the already published Cartoon Introduction to Economics, I realised this was the style of Grady Klein (as reflected below).

Thus, I have to assume this is how The Cartoon Introduction to Statistics will look like  when published in July… Actually, I received later a second copy of the definitive version, so I can guarantee this is the case. (Funny enough, there is a supportive quote of the author of Naked Statistics on the back-cover!) I am quite perplexed by the whole project. First, I do not see how a newcomer to the field can learn better from a cartoon with an average four sentences per page than from a regular introductory textbook. Cartoons introduce an element of fun into the explanation, with jokes and (irrelevant) side stories, but they are also distracting as readers are not always in  a position to know what matters and what does not. Second, as the drawings are done in a rough style, I find this increases the potential for confusion. For instance, the above cover reproduces an example linking the histogram of a sample of averages and the normal distribution. If a reader has never heard of histograms, I do not see how he or she could gather how they are constructed in practice. The width of the bags is related to the number of persons in each bag (50 random Americans) in the story, while it should be related to the inverse of the square root of this number in the theory.  Similarly, I find the explanation about confidence intervals lacking: when trying to reassure the readers about the fact that any given random sample from a population might be misleading, the authors state that “in the long run most cans [of worms] have averages in the clump under the hump [of the normal pdf]”. This is not reassuring at all: when using confidence intervals based on 10 or on 10⁵ normal observations, the corresponding 95% confidence intervals on their mean both have 95% chances to contain the true mean. The long run aspect refers to the repeated use of those intervals. (I am not even mentioning the classical fallacy of stating that “we are 99.7% confident that the population average is somewhere between -1.73 and -0.27″…)

In conclusion, I remember buying an illustrated entry to Marx’ Das Kapital when I started economics in graduate school (as a minor). This gave me a very quick idea of the purpose of the book. However, I read through the whole book to understand (or try to understand) Marx’ analysis of the economy. And the introduction did not help much in this regard. In the present setting, we are dealing with statistics, not economics, not philosophy. Having read a cartoon about the average length of worms within a can of worms is not going to help much in understanding the Central Limit Theorem and the subsequent derivation of confidence intervals. The validation of statistical methods is done through mathematics, which provides a formal language cartoons cannot reproduce.