My new book is out today: a collection of columns, journalism and essays, but also some of my more colourful government reports, academic papers, and more.

It looks lovely.

Here is the introduction.

Hooray!

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Introduction

This is a collection of my most fun fights: but the fighting is just an excuse. There’s nothing complicated about science, and people can understand anything, if they’re sufficiently motivated. Coincidentally, people like fights. That’s why I’ve spent the last ten years lashing science to mockery: it’s the cleanest way I know to help people see the joy of statistics, and the fascinating ways that evidence can be distorted or ignored.

But these aren’t personal attacks, and I’m not an angry person. All too often, people hoping to make science accessible fall into the trap of triumphalism, presenting science as a canon, and a collection of true facts. In reality, science is about the squabble. Every fight you will read in this book, over the meaning of some data, is the story of the scientific process itself: you present your idea, you present your evidence, and we all take turns to try and pull them both apart. This process of close critical appraisal isn’t something we tolerate reluctantly, in science, with a grudge: far from it. Criticism and close examination of evidence is actively welcomed – it is the absolute core of the process – because ideas only exist to be pulled apart, and this is how we spiral in on the truth.

Away from the newspapers and science TV shows you can see that process, very clearly, in the institutions of science. The question-and-answer session at any academic conference, after someone presents their scientific research, is often a bloodbath: but nobody’s resentful, everyone expects it, and we all consent to it, as a kind of intellectual S&M activity. We know it’s good for our souls. If the idea survives, then great; if it needs more evidence, we decide what studies are needed next and do them. Then we all come back next year, tear the evidence apart again, and have another think. Real scientists know this. Only the fakers cry foul.

In short, this book has a manifesto: check the evidence and fight back against anyone who tries to stop you. Along the way, you will get a grounding in statistics, study design, evidence-based policy and much more, in bite-size chunks. Because while my last two books – Bad Science and Bad Pharma – were polemics with a shape, this is a racing collection of short pieces. As such, I hope it works as a kind of statistics toilet book, bringing satisfaction in short bursts, with a fight and an idea in each one.

So in the section of this book on surveys, we laugh at the stupidity of the nuclear power industry, some silly anti-abortionists and StoneWall (whom I actually adore). Or, if you prefer: we learn about the distortions of ‘participant bias’, misleading question design and a sticky problem involving a complex time-dependent variable. In the first piece of the book we cover some surprisingly unprofessional behaviour from a Baroness, Professor and previous Director of the Royal Institution. Or, if you prefer: we cover post-publication peer review and why the conventions of academic journals are helpful.

These pieces cover two decades of work. There are lots of Guardian columns, but also academic papers, a report for the UK education minister, my work in the Romney, Hythe and Dimchurch Railway Guidebook, the odd undergraduate essay and more. If I’m honest, it’s pretty soulful (for me, not you) looking back over two decades and seeing what has changed. I was in my twenties and barely out of medical school when I started writing a column in the Guardian. As time passed, the targets got bigger, my day job took me through postgraduate qualifications and grown-up battles, and I think I got better at pulling claims apart. There was also discipline from outside: writing about other people’s misdeeds, collecting ever greater numbers of increasingly powerful enemies – and all under British libel law – is like doing pop science with a gun to your head. So for that, thanks.

At the end I might tell you a little about how I work, why I do what I do, who made me, and how things have changed over the past two decades. For now, let’s just say I’m very grateful to all the many companies and people who, by their optimistically bad behaviour under fire, have given narrative colour to what might otherwise have been some very dry explanations of basic statistical principles.

What’s in this book

I’ve written 500,000 words in the last decade, so there is no rep- etition, and the corpses of folk like Gillian McKeith, the homeopaths and Big Pharma are left in my previous two books (although these characters fight on, like zombies, in the real world). My academic work on statins and Big Data is saved for a fun project that will be launching shortly. Lastly, most of my writing on randomised trials in education, policing and everywhere else is held back, as my book on this topic will come out in due course.

There is, however, some structure to this school reunion. In How SCIENCE WORKS we cover peer review, how research is unpicked and critiqued after publication, how we deal with contradictory research, the importance of methods and results being freely available, whether it matters who a researcher is, how cherry-picking harms science, and how myths are made when inconvenient results are ignored.

In BIOLOGISING we cover crass reductionism, including the peculiar beliefs that pain is only real when we scan see it on a brain scanner, that misery is best thought of as molecular, and that girls like pink ‘because they evolved to look for berries’. In STATISTICS we start with easy maths and accelerate painlessly to some fairly advanced notions. We cover why the odds of three siblings sharing a birthday is not 48,627,125 to 1, why spying on us all to spot the occasional terrorist is highly unlikely to work, how statistical tools for fraud helped catch Greece faking its national economic data, what you can tell from a change in abortion rates for Down’s syndrome, the many ways you can slice data to get the answer you want, the hazards of looking for spatial patterns on maps, and the most core statistical skill of all: how we can detect a true signal from everyday variation in background noise.

Then we go on to the glory of BIG DATA, the battles with government to get hold of it, the risks of sharing medical records with all and sundry, and the magical way that patterns emerge from the formless static of everyday life when you have huge numbers. In SURVEYS we learn the tricks of a sticky trade, and then we shift up a gear to cover EPIDEMIOLOGY, my day job, the science of spotting patterns in disease. Here we see how clever things called funnel plots can help to show whether one area’s healthcare really is any worse than another’s, whether an increase in antidepressant prescriptions really does mean more people are depressed (or even whether more people are taking antidepressants), and the core skill of all epidemiology: how to correct for ‘confounding variables’, or rather: how to make sure that apparent correlations in your data are real. In an overview of bicycle helmet research, we review every epidemiological error in the textbooks, and a grand claim about the benefits of screening for diseases helps show that doing something – even something small – can often be worse than doing nothing at all. We see why different study designs are needed to research common and rare diseases, and how frail memories can distort the findings, why we should never assume that laboratory tests are correlated with real patients’ suffering, and how simple blinded experiments can spot if a £70 wine magnetiser really does change the flavour.

In the section on BAD ACADEMIA, we see how whole fields have been undermined by the simple misuse of statistics. We find one simple statistical error made in half of all neuroscience papers, and, by using forensic methods, we can see that brain-imaging researchers must be up to no good, because collectively they are publishing far more positive findings than the overall numbers of participants in their research could possibly, plausibly, statistically, sustain. We see bad behaviour around journals retracting papers, and appallingly poor standards in animal research, alongside academic journals publishing wildly crass papers on how, for example, people with Down’s syndrome really are a bit like the Chinese.

In GOVERNMENT STATISTICS we see ludicrous over-claiming around public and private sector salaries (where commentators fail to compare like with like), Home Office figures on child abuse pulled almost from thin air, a government figure on the cost of piracy that assumes everyone in the country should be spending £9,700 each on DVDs and music every year, crime prevention numbers to support a national DNA database that simply do not add up, and a headline figure on local council overspending, from the Department for Communities and Local Government, whose derivation is so offensively stupid it almost defies belief. We also see there is no evidence that hosting events like the Olympics has any health benefit for the host nation.

EVIDENCE-BASED POLICY is a slightly different fish: is there really good evidence for the policies that governments choose? Here we see that the evidence supporting the redisorganisation of the NHS is weak and that the figures on poor performance in the NHS used to justify it are over a decade old, and when the minister tries to argue back, he digs a very deep hole. We see how a historic failure to run simple randomised trials on policy issues has left us ignorant on basic questions about what works, and then whizz through a few simple questions, showing how evidence can be checked for each one: is porn in sperm donor clinics a good idea, is organic food really better, is it wise for the Catholic Church to campaign against condoms, and are exams really getting easier? We see a thinktank report on maths, promoted by a TV maths professor, that gets its own maths catastrophically wrong, and a select committee misleading, and being misled. After all this carping, in a report for the Department For Education I set out how the teaching profession could have its own evidence-based practice revolution to mirror what we’ve seen in medicine (and review, along the way, how senior doctors as late as the 1970s fought back, to defend that favourite of the old and powerful: eminence-based medicine).

Recreational DRUGS are a magnet for bad policy, because ideology often conflicts with the evidence, so the temptation to distort the data is powerful. Here we see wildly inflated government figures for crop captures in Afghanistan (with a minister claiming that peasant farmers receive the entire street price from every £10 bag of heroin sold in London), and ask why death was quietly dropped from the government’s measures of drug-policy success, before an essay explaining why the UK prescribed heroin for heroin addicts from the 1920s onwards, why we stopped and why we should start again.

LIBEL is a subject close to my heart, having been through the process too many times. In this section, we see how the people who sue tend not to be very nice, and how their legal aggression can – to my great pleasure – backfire. This section also includes breast-enhancement cream, and the brief return of Gillian McKeith.

I’ve always railed against the idea that QUACKS are manipulators, with innocent victims for customers: one woman’s trip to intensive care presents an opportunity to see where the blame really lies, when quacks have their magical beliefs routinely reinforced by journalists and the government. More than that, we see how serious organisations – from universities to medicines regulators – can fail to uphold their own stated values when under political pressure or seduced by money. Then we have a brief interlude to look at three peculiarly enduring themes in modern culture: MAGIC BOXES of secret electronic components with supernatural powers (to detect bombs, cure cigarette addiction and even find murdered children), AIDS denialism (at the Spectator, of all places), and, in ELECTROSENSITIVITY, people eager to claim that electrical fields make you unwell (while selling you expensive equipment to protect yourself, and seducing jour-nalists from broadsheets to the BBC’s Panorama).

If science is about the quest for truth, then equally important is the science of IRRATIONALITY – how and why our hunches get things wrong – because that’s the reason we need fair experiments and careful statistics in the first place. Here we see how our intuitions about whether a treatment works can be affected by the way the numbers are presented, how our outrage is lower when a criminal has more victims, why blind auditions can help combat sexism in orchestras, how people can turn their back on all of science when some evidence challenges just one of their prejudices, how people win more in a simple game when they’re told they’ve got a lucky ball, how responding to a smear can reinforce it, how smokers are misled by cigarette packaging, how people can convince themselves that patients in comas are communicating, and how negative beliefs can make people experience horrible side effects, even when they’re only taking sugar pills with no medicine in them. In this section I also unwisely disclose my own positive and creative visualisation ritual, and the evidence behind it.

In BAD JOURNALISM we see the many different ways that journalists can distort scientific findings: misrepresenting an MSc student’s dissertation project with a headline that claims scientists are blaming women for their own rape, creating vaccine scares, and saying that exercise makes you fat. We also see the techniques journalists use to mislead, by burying the caveats and failing to link to primary sources, then we review research showing that academic press releases are often to blame, and that crass reporting on suicide can create copy-cat behaviour. The work in this section has made me extremely unpopular with whole chunks of the media, but I truly don’t think there’s anything personal here: the pieces are simply straight explanations, illustrating how evidence has been misrepresented by professional people with huge public influence. In light of that, I’ve included some attacks on me by others, and you can make what you will of their backlash. Lastly, we see how hit TV science series BRAINIAC – which sells itself on doing truly dangerous, really ‘real’ science – simply fakes explosions with cheap stage effects.

In the final furlong, there’s a collection of STUFF: my affectionate introduction in the guidebook of a miniature steam railway that takes you through council estates to the foot of a nuclear power station, and a guide to stalking your girlfriend through her mobile phone (with permission). Lastly there are some EARLY SNARKS. Reading your own work from ten years ago is a bit like being tied down, with your eyelids glued open, and forced to watch ten-foot videos of yourself saying stupid things with bad hair. But in case you miss the child I once was, here I take pops at cosmetics companies selling ‘trionated particles’, do the maths on oxygenated water that would drown you before it did any good, and cry at finding New Scientist being taken in by some obviously fake artificial intelligence software.

So welcome, again, to my epidemiology and statistics toilet book. By the simple act of keeping this book next to the loo you will – I can guarantee it – develop a clear understanding of almost all the key issues in statistics and study design. Your knowledge will outdo that of many working scientists and doctors, trapped in the silo of their specialist subjects. You will be funny at parties and useful at work, and the trionated ink molecules embedded in every page will make you youthful, beautiful and politically astute.

I hope these small packages bring you satisfaction.

Ben Goldacre, London, August 2014

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