Skip to main content

The Art of Uncertainty - David Spiegelhalter *****

There's something odd about this chunky book on probability - the title doesn't mention the P word at all. This is because David Spiegelhalter (Professor Sir David to give him his full title) has what some mathematicians would consider a controversial viewpoint. As he puts it 'all probabilities are judgements expressing personal uncertainty.' He strongly (and convincingly) argues that while the mathematical approach to probability is about concrete, factual values, outside of the 'natural' probabilities behind quantum effects, almost all real world probability is a subjective experience, better described by more subjective terms like uncertainty, chance and luck.

A classic way to distinguish between those taking the frequentist approach to probability and the Bayesian approach is their attitude to what the probability is of a fair coin coming up heads or tails after the coin has been tossed but before we have looked at it. The frequentist would say it's definitely heads or tails, but we can't say which. The Bayesian would say it's still 50:50 because we don't have any information yet. Spiegelhalter puts himself firmly into the Bayesian camp. However, even the most rabid frequentist could not find issue with Spiegelhalter's careful and detailed introduction to the nature of probability and how we use it.

There are plenty of real world examples here, from Covid-19 risks to picking socks at random from a drawer. Spiegelhalter provides us with a range of stories to back these examples up, making large parts of the content highly readable. If I have a criticism, I think the book is too long and could have had a tighter structure. I felt myself drifting away from interest and skipping through a few pages (it is over 400 pages long) occasionally - but always came back into focus as a new topic was covered.

As was the case with its earlier companion, The Art of Statistics, this is not going to turn you into an expert. Although there is some gentle mathematics, there is nothing more complicated than getting your head around conditional probability representations - but there is no doubt that reading the book will give you a better idea of what probability is, how it's used and abused, and why we can be more precise about some predictions than others. You will have to work a little to absorb what's in here - but it's worth the effort.

I think this pair of books should become classics, very much in the tradition of the Pelican imprint, which always been intended to inform non-experts without patronising. If you've ever heard Spiegelhalter speak, everything is put across in a warm, favourite uncle fashion - this is the case with the best parts of his writing too. It's a voice of reason in an area that can sometimes seem counter-intuitive, and it is very welcome.

Hardback:   
Kindle 
Using these links earns us commission at no cost to you
These articles will always be free - but if you'd like to support my online work, consider buying a virtual coffee:
Review by Brian Clegg - See all Brian's online articles or subscribe to a weekly email free here

Comments

Popular posts from this blog

Math for English Majors - Ben Orlin *****

Ben Orlin makes the interesting observation that the majority of people give up on understanding maths at some point, from fractions or algebra all the way through to tensors. At that stage they either give up entirely or operate the maths mechanically without understanding what they are doing. In this light-hearted take, Orlin does a great job of taking on mathematical processes a step at a time, in part making parallels with the structure of language. Many popular maths books shy away from the actual mathematical representations, going instead for verbal approximations. Orlin doesn't do this, but makes use of those linguistic similes and different ways of looking at the processes involved to help understanding. He also includes self-admittedly awful (but entertaining) drawings and stories from his experience as a long-time maths teacher. To make those parallels, Orlin refers to numbers as nouns, operations as verbs (though he points out that there are some flaws in this simile) a

Everything is Predictable - Tom Chivers *****

There's a stereotype of computer users: Mac users are creative and cool, while PC users are businesslike and unimaginative. Less well-known is that the world of statistics has an equivalent division. Bayesians are the Mac users of the stats world, where frequentists are the PC people. This book sets out to show why Bayesians are not just cool, but also mostly right. Tom Chivers does an excellent job of giving us some historical background, then dives into two key aspects of the use of statistics. These are in science, where the standard approach is frequentist and Bayes only creeps into a few specific applications, such as the accuracy of medical tests, and in decision theory where Bayes is dominant. If this all sounds very dry and unexciting, it's quite the reverse. I admit, I love probability and statistics, and I am something of a closet Bayesian*), but Chivers' light and entertaining style means that what could have been the mathematical equivalent of debating angels on

2040 (SF) - Pedro Domingos ****

This is in many ways an excellent SF satire - Pedro Domingos never forgets that part of his job as a fiction writer is to keep the reader engaged with the plot, and it's a fascinating one. There is one fly in the ointment in the form of a step into heavy-handed humour that takes away its believability - satire should push the boundaries but not become totally ludicrous. But because the rest of it is so good, I can forgive it. The setting is the 2040 US presidential election, where one of the candidates is an AI-powered robot. The AI is the important bit - the robot is just there to give it a more human presence. This is a timely idea in its own right, but it gives Domingos an opportunity not just to include some of the limits and possibilities of generative AI, but also to take a poke at the nature of Silicon Valley startups, and of IT mega-companies and their worryingly powerful (and potentially deranged) leaders. Domingos knows his stuff on AI as a professor of computer science w