A skeptic over coffee: sick of lab meetings


This post brought to you by a dedicated community of human Rhinovirus ( pdb model 1AYM).

Imagine the following dialogue between researchers:

Wayne the Brain: “Third one this week ::Cough:: I am literally sick of lab meetings.”
Wankdorf: “Oh I feel ya. There are way too many lab meetings. It’s a real waste of time, but that’s the cost of pulling from so many different realms of expertise in interdisciplinary projects.”
Wayne the Brain: “No no no, I am literally sick of lab meetings. All the exposure is really taking a toll on my health. ”
Wankdorf: “Why didn’t you say so?! Stay away, you purveyor of vile pestilence! ::cough::”

I hope, dear reader, that you spotted the root cause of their misunderstanding. Wayne (the Brain) was hypothesizing a suspected transmission rate while simultaneously advertising his own condition as definitely infected and possibly contagious. Wankdorf (unsurprisingly) misinterprets the statement by applying a more colloquial definition of the term “literally.” It’s not clear whether infection of the second researcher could have been avoided and the spread of the disease slowed had they practised more effective communication, but that scenario is plausible given what we know.

Of course this is an extreme example, and the consequences may not always be so dire. The most frustrating part of the above exchange and subsequent misunderstanding is that neither participant was strictly wrong in the definition they assumed for “literally.” This word now literally can be used to say “in the truest sense of the words” and the exact opposite, and my brain literally imploded when I learned about the new definition.

If you don’t believe me, check out the definition in both the Cambridge and Merriam-Webster online dictionaries. I’ve screenshotted the definitions to preserve this embarrassment for posterity:



Language is dynamic, some (Wankdorf etc.,) would even say that it is dynamical. Hence it doesn’t make you appear smarter to bore your friends by talking about Romans every time they say “decimate.” Language is constantly changing in response to the selective pressures of popular usage, subject to many factors as people and cultures interact.

Similar to many other examples of evolution, humans affect the way a language changes by taking note of and modifying the selective pressures they individually exert. The consequences may be particularly important in science, where English is the common tongue but not in general the first language of most practitioners. I expect that modern English will evolve to encompass multiple forms based on usage. Native speakers sat on the British Isles, laying in North America, and so on will continue to retain and invent complexity and idiosyncrasy, while international English will come to resemble a utilitarian version of Up-Goer Five English, paring off superfluous complexities while retaining the most effective elements to become as simple as possible, but no simpler. It’s possible that international English will even retain sarcasm.

Pop quiz: what’s your favourite English speaker idiosyncrasies used in this article?


Why is there no confidence in science journalism?


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 participating in 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&gt;

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&gt;

Stop Saying Dynamical


Following close behind experimental testing of falsifiable hypotheses, the secondary responsibility of a scientist is arguably clear communication of results. Given that the majority of research is ultimately funded by the tax-paying public, it is important that outcomes are eventually conveyed in a manner that can be understood by an intelligent layperson. Increased scientific literacy in policy makers and their constituents is a prerequisite to face modern challenges such as changing climate, public health, and the consequences of population pressure. Effective outreach to the public is more important than ever. Accepting the previous statement, why is there a continuing trend among scientists to mask communicative content through cryptic language, particularly when perfectly acceptable and widely recognized terms are available? I’ll focus on what I consider to be the most obvious and ridiculous offender, the great scourge of scientific writing, faculty information pages, and grant proposals; the great occluder of meaning, intimidator of readers, the entirely redundant bit of lexicon: dynamical.

Dynamical, like its more accessible and less attention-hungry sibling word dynamic, has its roots in the Greek dynamikos, meaning powerful. In general both terms relate to something that changes with time. Since both “dynamical” and “dynamic” function as adjectives, they are essentially interchangeable, the only difference between that I have ever been able to discern is the demographics of their use. “Dynamical” is used by physicists, mathematicians and engineers who work in dynamical systems theory, a branch of mathematics dealing with systems described by differential (if continuous) or difference (if discrete) equations. The additional suffix “-al” that delineates the two words seems to have been born of single, somewhat malicious intent: to serve as brick and mortar in the construction of an ivory tower separating scientists and small-folk. It is exactly this sort of word choice that leads to the perception that scientists have more smarts than sense and that they produce results that ultimately fail to have any application to the real world. Ultimately this serves as fuel for the anti-science fire burning through the minds of policy makers and the public. Consider the following two sentences and the impression they would leave on a reader over a morning coffee:

“We utilize the time-slice method as a means of dynamical downscaling to specify expected climate change for Southern Europe”

“We utilize the time-slice method as a means of dynamic downscaling to specify expected climate change for Southern Europe”


U. Cubaschll, H. von Storch, J. waszkewitz, E. zorita. Estimates of climate change in Southern Europe derived from dynamical climate model output . Climate Research . November 29 , 1996.

Even though the sentence makes reference to specific methods that a non-specialist reader might not be familiar with, the language is descriptive enough to impart a conceptual understanding of what the authors describe, except for that cumbersome “dynamical,” which throws the whole thing into question. It reads as if it came from a humour piece poking fun at absent-minded professor types. The null-meaning suffix implies there is meaning above and beyond the root word where there is none, it just sounds more complicated. This is not an outcome that scientists should strive for, no matter how intelligent it makes them feel to use it.

As disciplines in life science become increasingly concerned with complexity and modeling, I expect the number of life scientists interested in studying dynamic systems will only continue to rise. Given the particularly relevant nature of life sciences to understanding our relationship to our living planet, I beg you, wherever possible, to avoid using the word dynamical. The physicists, mathematicians, and engineers may be entrenched in their devotion to the nonsense word, but there’s no reason for this senseless departure from clarity to infect biologists, ecologists, biochemists, etc. any more than it already has. The arbitrary and counterintuitive way that scientists name the genes they discover-a combination of sarcasm, mystery and the opposite of their function-is a big enough mess.


Consider this an invitation to attempt to delineate the dynamic/dynamical word pair in the comments.