It hasn’t been a year, but Springer issued its annual report, and, in my eyes anyway, Uncertainty is a hit!
From 30 June 2016 through the first two weeks of April 2017, 821 copies were sold. And more were downloaded—but I don’t know how many. Springer has a licensing deal with many major universities and institutions, which provides on-demand ebooks for members. Uncertainty is one of these books. I have had reports from a good handful of people that they have downloaded it, but that’s all I know. (I don’t get separate royalties from downloaded copies.)
Do not forget Uncertainty’s main page where posts related to the book are indexed (like with most things, I am running behind on this).
I’ve had conversations with many about various points in the book. I haven’t seen anywhere where I changed my mind. But I’ve seen many places where I could have added more explanation, spent more time editing and writing, and I have been told I need add discussions on several crucial matters. Sample size, for instance (relate it to real, not statistical, control). Factor analysis (don’t do it). And so on. Keep the questions coming and I’ll get to them—eventually.
Uncertainty’s been reviewed at The Philosopher by Thomas Scarborough. Bad news first:
I have one demurral ato make. In places, the style seems unnecessarily to get in the way of the content. In particular, outbursts such as ‘Die, p-value, die, die, die!‘ or ‘p-values, God rot them!’, while they are certainly memorable, do not seem to serve the book well as the serious academic work that it is.
I agree. The “die die die” was a joke, which is now obvious of too obscure an origin (think back to Usenet days and you’ll have it). So I wish it were gone. There are an appalling number of typos, too, which is surprising not because of me (I’m famous for them), but because the book was edited by Springer. I will correct these for any second edition.
Now for better bits.
[Uncertainty], writes the author, is not answered by grasping for equations, let alone models. It requires ‘slow, maturing thought’. It is more a matter of philosophy than of mathematics. Yet people shun the effort. Instead, they grasp at pre-packaged probability theory, which is far too easily applied without further thought. In fact, the author sketches a situation of crisis proportions. There is altogether too much that we get wrong…
The publisher describes this work as a textbook. It begins with what one might call a componential analysis of probability. It carefully examines such concepts as truth, induction, chance — and many besides. Then it applies these observations to the field of modelling. While the mathematics are complicated, this is compensated for by the authors’s gift of explanation.
The book really brightens up when one reaches worked examples of what can and does go wrong, and how probability calculations for the self same situations may easily turn out to be quite different. The examples are generalised, too, so as to be meaningful beyond specific contexts. Some particularly illuminating sections of the book include a series of graphs and equations in which the quantification of GPAs, the probabilities of developing cancer, or how one might validate homophobia, are discussed…
All in all, if the author is right, then our world has strayed down a path which is dangerously simplistic — and this tendency towards simplistic thinking has much to do with how we think about uncertainty. One might go so far as to say: that we have misapplied, and continue to misapply, theory which has to do with things of critical importance, including the very future of humanity.
This Scarborough fellow is on to something. Better buy the book to discover what.
I’ll leave the last word to commenter Keith:
William Briggs’s book sounds intriguing, and an important read; there’s so much to the story of probability and chaos. Most people are familiar with the frequently recounted story involving the mathematician-cum-meteorologist Edward Lorenz, who used computer models to predict weather — and in the process serendipitously contributed to the development of chaos theory, and how scientists look at such exquisitely nonlinear systems as the weather. Back in the early Sixties, he decided to rerun one of his weather simulations. However, not thinking it mattered, Lorenz decided to begin the simulation in the middle, using numbers (for the ‘initial conditions’) from the first run. Much to his astonishment, the new virtual weather pattern, which he expected to follow the first run of the model, dramatically deviated from it. What he subsequently realized is that whereas the computer had stored in its memory the first run’s results to six decimal places, the printout, from which he reentered the numbers, had truncated the numbers to just three decimal places, to save space. As for predictions, modeling, nonlinearity, probability, initial conditions, uncertainty, controls, chaos, and outcomes, the rest is history. I look forward to reading this book.