If we have seen further, it’s largely by standing on layers of previous errors.
I had a lecture last week about cognitive biases, their possible adaptiveness and also impacts on science. It also led me to think about the old “hyper-competent scientist” trope so typical for SF. Science-fictional scientists can often recite complex information verbatim and know the answer to every question, even if it’s unrelated to their subfield of research – but then again, there are few molecular biologists focused on studying only one class of receptors in SF. Science-fictional scientists are usually either “generalists”, or very well-informed about basically every single subject of their field. A biologist can easily identify any plant or animal, run various analyses, create a model of a protein’s active site as well as an ecosystem simulation. And if they by chance don’t know something, they are able to quickly look the relevant information up or find out.
When someone asks me what is that beautiful meadow flower, I usually shrug and admit I have no idea. There had been times when I remembered hundreds of plants with their Czech as well as Latin names, but that used to be when I was around ten years old and still could recall a lot from a garden plant lexicon I had read when I had had nothing else to read. These data long since made room for other information in my long-term memory; these neural pathways weren’t being used often enough. I can still look it up but I’m no botanist. And if you ask me about some particular protein, my most probable anwer would be: “Let me google it.”
Most scientists aren’t infinite data banks respective to their fields. Moreover, there is a growing tendency is to specialize. Studying one protein class your whole life is nothing unusual – on the contrary. And while scientists should be very good at looking up information, don’t expect it so instantaneous as shown in fiction. If a scientist wants to learn thoroughly about something that’s not their area of research, it’s probable they’ll spend numerous evenings reading papers they’ve found, comparing their results, drawing their own conclusions. Evidence is rarely pointing clearly in one direction in all relevant studies. The world is filled with noise.
Also, scientists in fiction never seem to struggle with bureaucracy, fight for grants, wonder which type of applicable statistical analyses is the best, or find themselves short of equipment or surrounded by misbehaving instruments… And they never seem to think about sample sizes and the time needed to prepare experiments and get sufficient volumes of data.
In fiction, science is usually easy and quick.
In reality, things can go wrong and often do.
Sometimes there’s an error caused by badly working equipment. Sometimes the noise filtering method introduces false patterns into the final data. Sometimes it’s the scientists who make errors or mistakes (which is often not the same).
I think most of us were quite skeptical when we had heard about the so-called “arsene bacteria” or “faster than light neutrinos”, which were revealed to be erroneous later. Some findings seem a bit less suspicous but also turn out to have been wrong. This year’s announcement of gravitational waves measurement turned out to have been likely caused by magnetically aligned cosmic dust.
Many people don’t seem to understand how such errors are possible and then either remain perplexed, or question the whole authority of science.
A big revelation, people: If we have seen further, it’s largely by standing on layers of previous errors.
Errors are a normal, hardly evitable part of the quest for knowledge, and they are useful in the way that they tell us what to be careful about and can help us point to the right direction then. They may be results by flawed measurements (for a variety of reasons), unsound methodology (which had seemed fine at the time), faulty interpretations of results, or just plain noise… There is a basically undending spectrum of ways how to inadvertently introduce flaws into experiments. They happen and can hardly be eliminated. The important part is that these flaws are discovered and identified. Replication of previous experiments is crucial (and one of today’s problems in some fields is the lack of it – look up an interesting paper titled “Why Most Published Research Findings Are False” and also a response “Most Published Research Findings Are False—But a Little Replication Goes a Long Way”; but now I digress a little from science in SF – this is a rich material for a whole another topic). Knowing about biases (especially confirmation bias and small sample bias) and trying to avoid them is an essential part of the process as well.
Caution is a constant companion if you want to avoid mistakes and errors. When I had dissected sandflies infected by sauroleishmanias during my undergrad studies, I was deeply worried about not puncturing the tiny insect’s gut, then turning the light and filter in the microscope just right to see the leishmanias well, making a good enough estimate of their count…
After earning my BSc., I arrived at a point of bifurcation: I couldn’t stay in both the parasitology group and the evolutionary biology group, where I was also involved, at the same time. Eventually, I remained the the latter. No more risk of possible clumsy sandfly dissections. A whole other area of possible mistakes to be aware of. My diploma thesis topic lies on the verge of evolutionary biology, behavioral science, experimental economy, psychology and game theory. So, you see, there is a lot to be wary about. I’m constructing the methology and seeing the results from a biologist’s perspective. An expert in any other of the fields might become horrified in the worst case. The way to avoid this worst possible outcome? Caution. Learning. Double-checking. Discussions with people outside my subjective field. And like everyone else, I try my best and sincerely hope that I’m not introducing any errors into the study. Sometimes, I’m plenty worried. But that’s good. That leads to double care and caution.
You may have noticed it wasn’t so hot with my specialization in my undergrad years. However, it’s not so extraordinary before graduate studies, a number students have it that way. Engaging in more fields of research becomes highly unusual if it’s later in one’s scientific career. I know some scientists who are truly great in more, often widely separated areas, and I admire them immensely.
Nevertheless, that isn’t how it usually works, although being a polymath is pretty typical for many science-fictional scientists. But polymaths are an essential, albeit rather rare parts of the scientific world. Ignoring complexity and uncertainty is not – or shouldn’t be. In most SF, you don’t hear about confidence intervals, statistical power, sample size, confounding variables… In SF, science usually seems like something that’s always immediately right. But, surprise: That’s rarely the case. Scientific results and their interpretations are not to be taken on faith, though they’re often reduced to that in school curricula. In ideal case, they seem most likely correct after some rounds of replication and independent tests pointing in the same direction. Even then, there are limitations. Few results can be broadly generalized (depends on what you’re studying). Some only apply for very special cases and can serve as a starting point for broader research in less constrained environments, but aren’t applicable directly (like a number of studies done on inbred strains of various model species). Hardly an element abundant in science fiction literature.
So, should SF embed more realistic depictions of science? It would certainly be a welcome change for me. Showing science isn’t crucial for SF stories but if the author decides to make it a part of the story, then it should be done well, like any other part of it. Portraying research more realistically can lead to more interesting, complex stories. It can create conflict and suspense, show the characters’ personalities in more detail, from an angle you probably don’t usually see. Good portrayal of science doesn’t exclude good portrayal of characters – on the contrary. I often hear about “idea-driven fiction vs. character-driven fiction”, but it seems like nonsense to me, a false dichotomy. Why on earth couldn’t good fiction have great ideas as well as great characters? Some readers and authors may think about science as something outside the story, outside entertaining the readership, useful only for necessary yet boring infodumps.
Wrong. In most of my favorite SF works, the authors managed to create multifaceted, interesting stories, societies, individual characters and ideas. Watts and Egan immediately come to my mind.
Or imagine any real-world science story, regardless whether it turned out to be successful or not. They’re full of adventure, accidents, determination, misjudgments, great insights, brand new ideas, falls, rises, routine as well as thrill and suspense… They’re not infodumps. The history of science reads like an immensely captivating story full of fascinating characters.
I may be a bit naive and sound terribly pompous if I say now that the quest for knowledge is the greatest adventure in the world but that’s my view. One side of the coin are the low salaries, trouble with equipment and analyses, hours spent revising grant proposals and worrying whether you’re really getting to something and not just joining the ranks of publishing flawed stuff… the other is the hope that you’re really up to something interesting and helping us know the world better. I must admit that’s one of the reasons I like SF: the sense of wonder at new discoveries, getting to know other life forms, celestial objects, strange chemical reactions, vastly different societies…
Science fiction may well be the greatest adventure in the world taken further and well-done portrayal of science can make it reach anywhere you wish. Let’s have that adventure.