Emma Lehmberg, PhD student
Rosenthal Lab, Texas A&M University

Currently, I am engaged in a truly arcane, largely benign dispute with my PhD advisor. 

In phylogenetics, sometimes you’ll see the phrase “true tree” written – this is the tree that represents the relationships as they have existed in nature. They are not hypotheses but the ideal truth of how these organisms have evolved. 

To my advisor, a true tree is undiscoverable because we cannot observe – or indirectly observe through precise modelling – the evolution of each taxon in a tree.  We can only ever estimate the relationships. I, on the other hand, believe that this tree is a possibility, that each phylogeny that is published with additional data is a step towards what these relationships truly are.

Ultimately, he may very well be correct – unless we invent a time machine, and follow populations through time, we may never know the relationships within a group of organisms. It’s a scientific ideal that we can strive towards while knowing we may never meet it. To me, this argument is a microcosmic example of the specialized language we use in science, and further, how it can be confusing to those outside of a field if taken at face value.

To those who study evolutionary relationships, the true tree is an ideal and not a reality, while to people like my family – who are intelligent, thoughtful people but not scientists – it could be taken as a verifiable truth. The generalized language we use is reassuring to those who want to trust science to teach them about the world, but it can be misleading.

So how do we navigate that? How do we communicate our work to a broad audience without losing nuance? 

A paper published in PNAS this year says we may not be able to without first examining ourselves. DeJesus et al. measured the use of generic language in psychology studies, defining “generic” as language that tends to smooth variability by making broad statements about something. For example, if I were to write that all mammals have a placenta, it leaves out the few that do not. In their paper, DeJesus et al show that generalisations are common in psychology and allow for easy communication to a general audience. However, these same generalisations can lead to the assumption that these statements are “normatively correct” and when communicating using language like this, authors often gloss over variation in their own sample size.

Though this paper addresses a single, well-publicised field, it’s possible that other fields may go about communicating their work in generalizations (a quick google search turned up no similar studies in evolution, but some in ecology). If this is true, it puts the onus on us as scientists to communicate our work effectively and with precise language that communicates the complexity of the fields we’re engaged in.

However, precise language can easily slide into jargon, making science inaccessible to a broad audience. To balance, DeJesus et al. suggest that addressing the assumptions and framework that the study was conducted under may help to introduce some nuance into the study. The American Society for Cell Biology suggests using metaphor or analogy to explain technical language that can’t be avoided; an audience will respond to something they can relate to or understand.

Like all things PhD, becoming an expert at effective language use and science communication comes down to practice. Try explaining it different ways and see what works, both with peers and with a broad audience. As Ms Frizzle says, “Take chances, make mistakes, get messy!”

Resources for Science Communication

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