Archive for not a book review

Data Science & Machine Learning book free for download

Posted in Statistics with tags , , , , , , , on November 30, 2020 by xi'an

statistical illiteracy

Posted in Statistics with tags , , , , , , , , , , , on October 27, 2020 by xi'an

An opinion tribune in the Guardian today about the importance of statistical literacy in these COVIdays, entitled “Statistical illiteracy isn’t a niche problem. During a pandemic, it can be fatal“, by Carlo Rovelli (a physics professor on Luminy campus) which, while well-intended, is not particularly helping. For instance, the tribune starts with a story of a cluster of a rare disease happening in a lab along with the warning that [Poisson] clusters also occur with uniform sampling. But.. being knowledgeable about the Poisson process may help in reducing the psychological stress within the lab only if the cluster size is compatible with the prevalence of the disease in the neighbourhood. Obviously, a poor understanding of randomness and statistical tools has not help with the handling of the pandemics by politicians, decision-makers, civil servants and doctors (although I would have added the fundamental misconception about scientific models which led most people to confuse the map with the territory and later cry wolf…)

Rovelli also cites Bruno de Finetti as “the key to understanding probability”, as a representation of one’s beliefs rather than a real thing. While I agree with this Bayesian perspective, I am unsure it will percolate well enough with the Guardian audience. And bring more confidence in the statistical statements made by experts…

It is only when I finished reading the column that I realised it was adapted from a book soon to appear by the author. And felt slightly cheated. [Obviously, I did not read it so this is NOT a book review!]

if then [reading a book self-review]

Posted in Statistics with tags , , , , , , , , , , , , , on October 26, 2020 by xi'an

Nature of 17 September 2020 has a somewhat surprising comment section where an author, Jill Lepore from Harvard University, actually summarises her own book, If Then: How the Simulmatics Corporation invented the Future. This book is the (hi)story of a precursor of Big Data Analytics, Simulmatics, which used as early as 1959 clustering and simulation to predict election results and if possible figure out discriminant variables. Which apparently contributed to John F. Kennedy’ s victory over Richard Nixon in 1960. Rather than admiring the analytic abilities of such precursors (!), the author is blaming them for election interference. A criticism that could apply to any kind of polling, properly or improperly conducted. The article also describes how Simulmatics went into advertising, econometrics and counter-insurgency, vainly trying to predict the occurence and location of riots (at home) and revolutions (abroad). And argues in a all-encompassing critique against any form of data-analytics applied to human behaviour. And praises the wisdom of 1968 protesters over current Silicon Valley researchers (whose bosses may have been among these 1968 protesters!)… (Stressing again that my comments come from reading and reacting to the above Nature article, not the book itself!)

the biggest bluff [not a book review]

Posted in Books with tags , , , , , , , , , , , on August 14, 2020 by xi'an

It came as a surprise to me that the book reviewed in the book review section of Nature of 25 June was a personal account of a professional poker player, The Biggest Bluff by Maria Konnikova.  (Surprise enough to write a blog entry!) As I see very little scientific impetus in studying the psychology of poker players and the associated decision making. Obviously, this is not a book review, but a review of the book review. (Although the NYT published a rather extensive extract of the book, from which I cannot detect anything deep from a game-theory viewpoint. Apart from the maybe-not-so-deep message that psychology matters a lot in poker…) Which does not bring much incentive for those uninterested (or worse) in money games like poker. Even when “a heap of Bayesian model-building [is] thrown in”, as the review mixes randomness and luck, while seeing the book as teaching the reader “how to play the game of life”, a type of self-improvement vending line one hardly expects to read in a scientific journal. (But again I have never understood the point in playing poker…)