First, a quick reminder on the vulnerability of science to easy-to-make statistical mistakes:

Science News has the scoop. Mainly dealing with medical and laboratory science, it retells a couple of important stories, e.g.

*In 2007, for instance, researchers combing the medical literature found numerous studies linking a total of 85 genetic variants in 70 different genes to acute coronary syndrome, a cluster of heart problems. When the researchers compared genetic tests of 811 patients that had the syndrome with a group of 650 (matched for sex and age) that didn’t, only one of the suspect gene variants turned up substantially more often in those with the syndrome — a number to be expected by chance.*

“Our null results provide no support for the hypothesis that any of the 85 genetic variants tested is a susceptibility factor” for the syndrome, the researchers reported in the Journal of the American Medical Association.and adds really good examples, e.g.

* Suppose a certain dog is known to bark constantly when hungry. But when well-fed, the dog barks less than 5 percent of the time. So if you assume for the null hypothesis that the dog is not hungry, the probability of observing the dog barking (given that hypothesis) is less than 5 percent. If you then actually do observe the dog barking, what is the likelihood that the null hypothesis is incorrect and the dog is in fact hungry?*

Answer: That probability cannot be computed with the information given. The dog barks 100 percent of the time when hungry, and less than 5 percent of the time when not hungry. To compute the likelihood of hunger, you need to know how often the dog is fed, information not provided by the mere observation of barking.This second example ties in well with

The Hockey Stick Illusion: Climategate and the Corruption of Science (Independent Minds), which is reviewed

here. It tells a really absorbing story: why the "hockey stick" illustration became so significant, and why it was wrong. Doesn't sound like a fun read? You'd be wrong. This is an example of writing about scientific detective work that is well-written, and worth a read. From the review:

*Montford’s book is written with grace and flair. Like all the best science writers, he knows that the secret is not to leave out the details (because this just results in platitudes and leaps of faith), but rather to make the details delicious, even to the most unmathematical reader. I never thought I would find myself unable to put a book down because—sad, but true—I wanted to know what happened next in an r-squared calculation. This book deserves to win prizes.*Pick it up!