Apples and Oranges
2 March 2022
The Scientific Method is (rightly) obsessed with fair comparisons.
The timing of drug treatments, batch variation in reagents, cell concentration across a 96-well plate, the number of replicates… all of these need to be carefully controlled, otherwise any comparison between experiments will be thrown out of the proverbial window.
This is such an important concept in scientific training that we’ve dedicated a whole video to measuring and normalising protein concentration, all in an effort to allow scientists to make apples to apples comparisons between different protein samples (as part 3 in our 3-part series on protein analysis, all linked below):
What is often overlooked are the “apples to oranges” comparisons scientists routinely make when communicating our work to the general public. In spite of our best scientific analogies and metaphors, the chasm between the complexity of science and the general public’s understanding can’t be easily bridged.
Can you explain this graph without using “jargon”?
Much of this boils down to how jargon-rich our scientific work is, but what counts as jargon in this day and age, and how much jargon is acceptable? There is a growing movement to improve the readability of scientific writing targeted for “non-experts”, and Rakedzon et al., 2017 have built an “automatic jargon identifier” for scientific writers. Their work speaks for itself, but involved compiling 90 million words published on the BBC website and validating it against 5000 pairs of scientific publications and their accompanying lay summaries. These efforts have been distilled into a free online tool “Science and Public De-Jargonizer”, all in the name of improving public accessibility to science and scientific writing.
I am by no means an expert in science communication, and have all too often relied on the crutch of jargon-intense scientific summaries. I’m a work in progress in this space, and that’s OK? We’re only ever as good as our last attempt in anything we try.
Jack.
