Living in the so-called anthropocene, meaningful participation in humanity’s trajectory requires scientific literacy. This requirement is a necessity at the population level, it is not enough for a small proportion of select individuals to develop this expertise, applying them only to the avenues of their own interest. Rather, a general understanding and use of the scientific method in forming actionable ideas for modern problems is a requisite for a public capable of steering policy along a survivable route. As an added benefit, scientific literacy produces a rarely avoided side-effect of knowing one or two things for certain, and touching upon the numinous of the universe.
Statistical literacy is a necessary foundation for building scientific literacy. Widespread confusion about the meaning of such terms as “statistical significance” (compounded by non-standard usage of the term “significance” on its own) abounds, resulting in little to no transferability of the import of these concepts when scientific results are described in mainstream publications. What’s worse, this results in a jaded public knowing just enough to twist the jargon of science to support their own predetermined, potentially dangerous, conclusions (e.g. because scientific theories can be refuted by evidence to the contrary, a given theory, no matter the level of support by existing data, can be ignored when forming personal and policy decisions).
I posit that a fair amount of the responsibility for improving the state of non-specialist scientific literacy lies with science journalists at all scales. The most popular science-branded media does little to nothing in imparting a sense of the scientific method, the context and contribution of published experiments, and the meaning of statistics underlying the claims. I suggest that a standardisation of language for describing scientific results is warranted, so that results and concepts can be communicated in an intuitive manner without resorting to condescension, as well as conferring the quantitative, comparable values used to form scientific conclusions.
A good place to start (though certainly not perfect) is the uncertainty guidance put out by the Intergovernmental Panel on Climate Change (IPCC). The IPCC reports benefit from translating statistical concepts of confidence and likelihood into intuitive terms without sacrificing the underlying quantitative meaning (mostly). In the IPCC AR5 report guidance on addressing uncertainty [pdf], likelihood statements of probability are standardised as follows:
In the fourth assessment report (AR4), the guidance [pdf] roughly calibrated confidence statements to a chance of being correct. I’ve written the guidance here in terms of p-values, or the chance that results are due to coincidence (p = 0.10 = 10% chance), but statistical tests producing other measurements of confidence were also covered.
The description of results via their confidence rather than statistical significance, which is normally used, is probably more intuitive to most people. Few people in general readership readily discern between statistical significance, i.e. the results are likely to not be due to chance, and meaningful significance, i.e. the results matter in some way. Likewise, statistical significance statements are not even very well established in scientific literature and vary widely by field. That being said, the IPCC’s AR4 guidance threshold for very high confidence is quite low. Many scientific results are only considered reportable at a p-value of less than 0.05, or 5% chance of being an experimental artifact in the data due to coincidence, whereas the AR4 guidance links a statement of very high confidence to anything with less than a 10% chance of being wrong. Likewise, a 5-in-10 chance of being correct hardly merits a statement of medium confidence in my opinion. Despite these limitations, I think the guidance should have been merely updated to better reflect the statistical reality of confidenceand it was a mistake for the guidance for AR5 to switch to purely qualitative standards for conveying confidence based on the table below, with highest confidence in the top right and lowest confidence in the bottom left.
Adoption (and adaptation) of standards like these in regular usage by journalist could do a lot to better the communication of science to a general readership. This would normalise field-variable technical jargon (e.g. sigma significance values in particle physics, p-values in biology) and reduce the need for daft analogies. Results described in this way would be amenable to meaningful comparison by generally interested but non-specialist audiences, while those with a little practice in statistics won’t be any less informed by dumbing-down the meaning.
Edited 2016/06/25 for a better title, added comic graphic. Source for file of cover design by Norman Saunders (Public Domain)
23 Aug. 2014: typo in first paragraph corrected:
. . . meaningful participation
in participatingin humanity’s trajectory. . .
Michael D. Mastrandrea et al. Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties. IPCC Cross-Working Group Meeting on Consistent Treatment of Uncertainties. Jasper Ridge, CA, USA 6-7 July 2010. <http://www.ipcc.ch/pdf/supporting-material/uncertainty-guidance-note.pdf>
IPCC. Guidance Notes for Lead Authors of the IPCC Fourth Assessment Report on Addressing Uncertainties. July 2005. <https://www.ipcc-wg1.unibe.ch/publications/supportingmaterial/uncertainty-guidance-note.pdf>