Writeblr Resources: Writing Realistic Scientists
This rant is brought to you by the sasquatch program I was listening to on the way home from work and how glibly it insisted that scientists were suppressing the evidence. While the theories that the sasquatch advocate threw out there were interesting in terms of story potential, they reminded me how few writers have a science background.
Personally, I have a BS in environmental science, i.e. being shuffled between the geography, biology, and chemistry departments for four years, and I’m currently employed as a microbiologist, so I do have that background. Have 10 (yes, 10) of my pet peeves.
1. The Omnidisciplinary Scientist, or as I like to call it, the comic book scientist. Scientists specialize heavily. You will get a grounding beyond the layperson’s high school coverage of fields related to yours if you go into a science, but it won’t be ALL fields of science. I have a less than high school level grasp of physics due to the fact that it was never required in my field. When you get really deep into technical stuff, however, two people in the same broad field might not know much about the other’s specialty. A particle physicist and an astrophysicist might only have a very basic grounding in each other’s experiments, though they’ve got a leg up on me in explaining them to each other.
A physicist telling a neuroscientist that they’ve discovered consciousness doesn’t read as good science, nor should you cite a dentist as a “scientist” in your argument about global warming.
2. Instant Results. CSI and other police procedurals are the primary culprits here, but also scifi tends to give people instant confirmation of what something is. Whether that’s germ identification, a blood test, or a chemical reaction, all experiments require setup time, controlled conditions, correct equipment, and analysis. If the machine does all the work in a few minutes, you don’t need a scientist.
For example: pregnancy tests used to take weeks, because urine samples from the potentially pregnant person had to be shipped to a lab, injected into frogs, and then the frogs had to be monitored to see if they released eggs. Now, you pee on a stick, because scientists spent years finding a quick chemical reaction (actually a change of reactions) that gave you a simple visible sign that specific hormones were present in your pee.
The frogs, presumably, are very relieved.
3. The insanely well-funded science lab. All experiments take money. Whether it’s for materials, equipment, or to pay an undergrad to count fruit flies every six hours, it’s just not plausible for most scientists to have every single piece of equipment they could ever need - and not all of the tools are publicly purchasable to begin with. My brother works with a biologist who has had to design a program to do statistics on bone shapes from almost scratch - when it’s done and they’ve published a paper on it, it will be publicly available, but until that happens, anyone who has to do the same analysis has to put years into developing the protocol themselves.
Also, as an example I’ve actually worked with, a Gas Chromatography Mass Spectrometer (a relatively common, if fancy, instrument in chemistry that can identify most chemicals with the right person running it,) can cost a hundred thousand dollars used. Routine maitenance (replacing a consumable part like the coil,) can cost hundreds of dollars, and if you use the machine more frequently you have to do it more frequently. And god forbid something goes wrong with the mechanical parts or the programming - it’s hardwired to a computer as old as your undergrad, and the last time they manufactured any replacement parts for the thing was 1986.
(If you want to hack one of these investment pieces of equipment by the way, forget about it - even something with a modern OS probably isn’t internet-enabled, as there is nothing that researchers hate more than waiting for an OS update before they can finish an experiment. Even relatively cheap instruments that run off a cheap modern laptop are pretty routinely debugged by having the wifi disabled, as nine times out of ten your program being messed up is because Windows updated. You have to physically go to the machine, put the files on a jump drive IF they are readable outside the program, and transfer them to something else. Or you can screenshot them and export the pics onto the jump file. Or copy them into excel and transfer the excel file.)
Addendum: hacking does not work like that. If you’ve seen it in a movie, it is either outdated in terms of computer science, or excessively dumbed down.
4. The Work dies with the scientist. If your work will be lost if you should meet with an unhappy fate, you are a supremely shitty scientist. First - very few fields that aren’t pure mathematics or computing can be undertaken solo. Academics have postdocs to do the analysis, grad students to do the specialized lab work, and undergrads to do the prep work. Businesses have PhD’s to do the final analysis, junior scientists to design and run experiments, and lab techs to clean up after them, and provide explanation for why the GCMS is disabled while windows updates. (Full disclosure: the reason I’m harping on this is because it happened to me with a spectrophotometer and I’m still not over whoever re-enabled the wifi.)
Also, though a company or secret shady government agency will not release your work for peer review the way an academic institution will, they will need the documentation of your work to file patents, or replicate it with the rest of their researchers.
If the field has merit and enough funding that other people will actually spend time on the same experiment, no one lone genius is the only person who could ever make a discovery. In fact, discoveries are independently replicated all the time, because most of them are enabled by other discoveries or new technology.
5. Absence of evidence is not evidence of absence. Science is a delicate balance between admitting that something *could* be happening and pointing out that just because I haven’t proven you wrong yet doesn’t mean that you’re right.
A plausible use of the absence of evidence: “We haven’t found any Higgs Bosons yet, but that does not mean that they don’t exist.” (As of 2013 they found some.) It’s plausible because all evidence suggested that the HB was possible, its existence strengthened longstanding theories that hundreds of people had failed to disprove and dozens of people had discovered supporting evidence of, and because it was something they could test for with the available technology.
An implausible use “We haven’t found any conclusive evidence of Sasquatches, but that doesn’t mean they don’t exist.” It’s implausible because we don’t have any credible evidence that they do exist - there is no longstanding theory or model that claims that the pacific northwest can support a significant population of bear sized hominid omnivores, and no plausible physical evidence that such creatures ever existed. It contradicts a whole host of theories in a lot of scientific disciplines: Ecology would posit that we would know about the role of such a large omnivore since they’d have a similar impact on local resources to an equivalent amount of bears, the recent fossil record and paleoarchaeology have yet to find any evidence that homonids other than anatomically modern humans have lived in north america, statistics would argue that if the creature is common enough and lives close enough to humans for sightings to ever be reported it would leave some evidence more credible than eyewitness testimony behind.
6. Contradiction is proof of being wrong. A single data point contradicting a theory is almost never an indication that the theory has been disproven. Science is done by humans, and mistakes are made. Similarly, a single success is not proof that you’re right. You need to do an experiment a lot to have enough data to be certain that what you think is happening is actually happening.
For example, those of you who took a statistics class can attest that just because you flipped a coin ten times and it landed on heads seven times doesn’t mean that you’re twice as likely to come up heads. You need to flip it a hundred times or more to have enough data to really do anything with it.
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