Vital statistics

Scientists, it turns out, are really bad at statistics. Numerous studies show that a startling proportion of academics consistently misunderstand the statistics they’re using, and the conclusions that can be drawn from them. A computer algorithm that highlights basic statistical errors was recently set loose on a huge sample of published research papers in psychology  and found that almost half contained a mathematical mistake; 13 per cent had a serious screw-up that meant their reported results might have been completely wrong. If scientists — who use statistics all day to analyse their experiments — are so innumerate, what hope is there for everyone else?

Enter Sir David Spiegelhalter, Winton professor for the public understanding of risk at Cambridge University. His new volume, The Art of Statistics, is in the great educational tradition of its publishing imprint, Pelican Books: an attempt to get everyone up to speed with the practical uses of statistics, without pages of terrifying equations or Greek letters. In a series of spry, airy chapters introducing the reader to probability, regression, causation, machine-learning and what’s nowadays called ‘data visualisation’ (also known as ‘making pretty graphs’), he succeeds fabulously.

Although it wouldn’t be a statistics book without a few dice-rolling analogies (thankfully it avoids references to poker, a common but never intuitive explanatory tool), the examples used are generally lively and well chosen. Does going to university really increase your risk of getting a brain tumour, as an already infamous scientific press release claimed in 2016? Unlikely. What was the probability that the skeleton dug up in a Leicester car park in 2012 was really that of Richard III? 0.999994, once you take carbon-dating and DNA evidence into account. Spiegelhalter provides all the statistical reasoning required to reach these conclusions, often circling back later to give another perspective on each problem.