Cancer patients, and their friends and families, are often faced with contradictory information on what to do about diet. On the one hand there are large numbers of mainstream oncologists and dieticians who tell patients to eat what they like so long as they keep the calories up and are able to get through chemotherapy or radiotherapy. On the other hand there are plenty of people who insist that only a strict vegan or macrobiotic or Gerson or other anti-cancer diet will help. And of course there are lots of books out there that advocate all kinds of diets, all of them claiming to be based on some sort of science. Those looking for a middle ground based on solid science are left trying to work out for themselves what makes sense and what is obvious nonsense.

Foods To Fight Cancer looks like lots of other books in the 'superfoods' genre. It's glossy, well illustrated and published by Dorling Kindersly. It looks more coffee table than operating table. However, unlike many of the anti-cancer food books that are on the market this one is written by scientists working in the field of diet and cancer and who are not only up-to-date with the science but who are engaged in making it happen. It just so happens that Richard Beliveau and Denis Gingras are excellent communicators able to write for the non-scientist as well as their colleagues.

The central premise of the book is that dietary interventions can help tip the odds against developing cancer, and also to aid in fighting cancer once it has started. The plant kingdom contains thousands of phytochemicals - polyphenols, terpenes, sulphides etc - which have potent anti-cancer properties. These micro-nutrients act in multi-faceted ways to block many of the different biological pathways necessary for cancers to form, grow and then metastasize. Unlike some of the over-inflated claims made by some, there is no promise of a single all-powerful cancer cure here. Instead the emphasis is firmly on looking at what pathways are necessary for cancer to develop and then what can be done to block these using multiple compounds from different foods. [Continued]

It will be a surprise for most people to discover that the world of statistics, like every other sphere of human life, has deep-seated ideological differences. We all know that economists, climatologists and physicists are prone to falling out with each other, but statisticians? But it is true, at the heart of statistics there are key philosophical disputes about the very meaning of probability. While everyone agrees that the chance of tossing a heads for a fair coin is 0.5, what that actually means will depend on whether you are a frequentist or a Bayesian, and if you're a Bayesian do you tend to the objectivist/logical or subjective schools? Understanding what these mean, and what the implications are as regards statistical thinking, is a key part of this entertaining and thought-provoking little book.

William Briggs is a research scientist, lecturer in statistics and the author of an engaging blog that is always worth seeking out. With this book Briggs sets out to provide the reader with a statistics course that aims to instil statistical thinking rather than providing a set of recipes to be followed. Where other statistics books take great delight in inflicting mind-numbingly tedious step-by-step algorithms on the reader, this book takes advantage of open-source stats software and instead focuses on what the stuff actually means.

This isn't to say that the book avoids math. Far from it, the reader is encouraged to think deeply about what he or she is doing. Statistical thinking is hard and the chances are that following pre-defined formulae is a recipe for getting things wrong. And, time and again, Briggs comes back to these differences between frequentist statistics (which is mostly what gets taught to students), and Bayesian analysis.[Continued]

What is 'statistical significance', 'standard error' and why should we care? For economists Deirdre McCloskey and Stephen Ziliak, these are fundamentally important questions and we should all care. As they put it:

Crossing frantically a busy street to save your child from certain death is a good gamble. Crossing frantically to get another mustard packet for your hot dog is not. The size of the potential loss if you don't hurry to save your child is larger, most will agree, than the potential loss if you don't get the mustard.

In other words, it's not the probability of success or failure that's important in the real world, it's the pay-off that counts. Translate this probability of success or failure into 'statistical significance' and you have the heart of the problem.

It would be fortunate if we could simply blame innumerate journalists and commentators for mistaking 'statistical significance' with actual significance in the real world. Unfortunately there is more to the confusion over the meaning of 'significance' than journalistic ignorance or laziness. It is, argue the authors, increasingly common in the sciences too - and with potentially dire consequences.

Statistical significance is a test that looks at whether a given result (for a sample) could have arisen by chance (normally 1 in 20). It doesn't ask whether the result is important in any way. Results can have high levels of statistical significance and yet be utterly trivial or uninteresting. And, let's be honest, the press (popular and scientific) is increasingly filled with reports of findings that are statistically significant and yet totally pointless. [Continued]

On the face of it a pop science book about data mining and linear regression hardly seems to be a recipe for a best seller, but that is precisely what Ian Ayres has managed to do with Super Crunchers. Subtitled 'How Anything Can Be Predicted', this slim little tome is a catalogue of anecdotes and stories that illustrate the ways in which large datasets, fast computers and clever algorithms can be combined to discover interesting and unexpected correlations buried deep in the numbers. And, all of this is done with minimal mathematical or technical content - this certainly isn't a book that is going to scare off the average mathematophobe.

Ayres primary thesis is that the conjunction of massive amounts of data, fast computers and statistical techniques means that ever more issues and decisions become amenable to analysis and fact-based decision-making. He looks at fields as diverse as wine-drinking, sports, economics and medicine to show how intuition and human expertise are being out-performed by numerical analysis. In many cases the number crunchers out-perform the old-fashioned expert by some considerable degree. This of course causes as well as solves problems, particularly when vested interests are at work. Who wants to be out-smarted by an equation?

Some of the most interesting material covers health-care and the quest for evidence-based medicine. This includes diagnostic assistance (what used to be called an expert system a long, long time ago), and moves that try and find evidence to back up the efficacy (or not) of common health-care procedures and protocols. [Continued]

Christopher Booker, co-author of Scared To Death, responds to questions on global warming, health scares, the mass media and responses to his book in this interview with LondonBookReview.com [Continued]

Nigel Calder, co-author of The Chilling Stars, responds to questions on the controversies surrounding his book, the film 'The Great Global Warming Swindle' and offers advice for the lay-person wanting to make sense of the competing theories of climate change. [Continued]

The Beautiful Tree by James Tooley; Introduction To Politics by Garner, Ferdinand and Lawson; Integrative Oncology by Donald Abrams and Andrew Weil…