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 As this was an unpublished version of the book, to appear in July 2013, I first assumed my copy was a draft version, with black and white drawings using limited precision graphics..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… 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.
