This article was published in Scientific American’s former blog network and reflects the views of the author, not necessarily those of Scientific American
"When Bill Gates walks into a bar... the average salary goes up." - Popular geeky stats joke.
I once heard a science editor at a rather well-known publication say, in public no less, that she has no idea what p-value* means. This came as a shock to me, a then-relative newcomer to the science communication sphere. Why shock? Because, as I once wrote, statistics “carries the purity of the sciences on its shoulders.” Indeed, as the scientific method is based entirely on statistics, not having a decent grasp of it leaves science reporting prone to serious mishaps.
Of course, I realised fairly quickly that not all science communicators are inept when it comes to statistics. But the countless examples of statistics misinterpretation by the media when reporting scientific findings and discoveries** (looking at you Daily Mail) clearly shows that there is much room for improvement.
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An upcoming book, written by members of the Scilance community, may help out in this regard. An excerpt from The Science Writers’ Handbook: Everything You Need to Know to Pitch, Publish, and Prosper in the Digital Age, published today exclusively by The Open Notebook, provides science writers with an important primer on statistics.
The excerpt is divided into three sections: “The Uncertainties of Uncertainty,” “Seeing the Story in the Stats” and “A Science Writer’s Statistical Phrasebook.” All three sections are important although I would like to highlight the second and third sections here. These two sections specifically draw attention to and attempt to explain potentially tricky statistical terminologies or concepts like percentage points, confidence intervals, absolute and relative risks and the infamous p-value. Admittedly, it’s all very basic and the explanations are sometimes a bit confusing but the sections do provide a decent base for science communicators to build on.
Up-and-coming science writers should be particularly receptive to statistics in the light of the “big data” era we’re entering. Scientific papers full of data and complex statistical methodologies will become increasingly prevalent and relevant in the near future. Consequently, adequate and critical science journalism will require a good statistics grounding more than ever before.
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* p-value, as the excerpt enticingly explains, is the “likelihood that the observed test result happened by chance. A low p-value means the results were significant and unlikely to have occurred by chance.”
** Science is not the only beat suffering from statistics misinterpretation. Sports, for instance, is currently going through a data revolution leading to a staggering amount of erroneous statistical analyses by sports writers who are casually interchanging correlation with causation.
