杏吧原创

Editorial: 鈥�Hope and hype in the world of statistics鈥�

TV kills, and so does socialised healthcare 鈥� that鈥檚 what the statistics say. Or do they? New 杏吧原创 explains how numbers can be turned into nonsense

TYPE the word 鈥渃ancer鈥� into the website search engine of the Daily Mail, a British tabloid newspaper, and a wealth of information is just a mouse click away. Some of the reports are calming, most alarming 鈥� and all come with figures to back them up. Women who use talcum powder are , says research. Cancer survival rates in the UK are , according to a study. The incidence of bowel cancer among the under-30s has , astonishing figures show.

The figures might make us worry for our health, but somehow we feel the better for their existence. Numbers help us make sense of the world: they speak of fact and certainty and the onward march of science. If you can put a number on a problem, then its extent is known and its impact can be circumscribed.

Yet that sense of solid certainty is all too often illusory. Statistics can be notoriously slippery, easily misused by the unscrupulous or misinterpreted by the unwary. Nowhere is that more true than in the field of human health.

That鈥檚 because the benefits of a particular medical treatment are often not obvious. 鈥淭here are very few miracle cures. Most treatments require careful science to determine if there is any benefit and how big the benefit is,鈥� says , a biostatistician at the University of Cambridge. 鈥淲orking out the effects of an environmental risk factor is even more tricky,鈥� he adds. Saying anything sensible about human health requires large, reproducible clinical trials, and the careful observation of diverse populations 鈥� all of which implies the use of statistical methods to extract workable conclusions from the data.

The British epidemiologist Austin Bradford Hill recognised this when, in 1946, he ran the first trials in which participants were randomly assigned to two groups, one of which received the treatment and one of which didn鈥檛. One of these trials tested the effectiveness of the antibiotic streptomycin to treat tuberculosis, a condition that Bradford Hill himself had developed while serving in the first world war. After just six months, the results were so convincing that they led to streptomycin being adopted as the standard treatment (). In 1950, together with Richard Doll, Bradford Hill similarly pioneered the use of statistical methods to provide the of a causative link between smoking and lung cancer.

Used well, then, statistics are a powerful tool. But caution is required. Sample size, the design of a study and even the definition of terms or the way a number is presented can all affect the value of the headline statistics we are offered. Generally, we are not privy to these details.

What鈥檚 more, the decisions we take concerning the well-being of ourselves or our loved ones are often made at times of intense emotional stress. 鈥淧eople are very much influenced by culture, emotions and values when making judgements, and that鈥檚 fine, that is part of being human,鈥� says Spiegelhalter. But it makes us all the more susceptible to seemingly incontrovertible numerical truths distilled into media headlines 鈥� and to the enthusiastic but sometimes equally misplaced insistence by researchers, doctors or advocates of a new treatment that it will do us good.

So when confronted with medical statistics, how do we know whether they are the real deal, or 鈥� wilfully or otherwise 鈥� distorted before they get to us? How do errors creep in? What are the questions we need to ask to avoid falling for them?

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