The division between art and science division is embedded in our culture. We think of science as exact, objective, following a strict method, whereas art as creative and subjective, with no formal rules. But this picture misunderstands what actual science looks like. In practice, science is not about following a strict, algorithm-like method, but, like in art, making judgement calls and thinking creatively, argues Ann Thresher.

There is an outdated idea that science and art are polar opposites. That science, associated with the left brain hemisphere, is logical, structured, whereas art, the domain of the right hemisphere is soft, intuitive, creative, guided by practiced judgement and innate skill. Of course, any neuro-scientist will tell you that the distinction between “right and left brain thinking” is a myth, that both sides are equally important in thinking through a math problem and painting a picture.

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Similarly, it is time to give up the myth that science and art are fundamentally different. Good science is an art-form, and scientists draw on highly trained, creative intuition and judgements, just as much as artists do. Scientists rely on the sort of soft skills we usually exclusively attribute to artists throughout their work, from conceptualising research projects, to designing experiments, to interpreting and presenting data, to conceiving of new theories and models. Science isn’t an exact science, in the sense that there is no one formula or methodology or approach that scientists just follow.  Science is a practice,  not unlike art - a messy mosaic of judgements and creativity, aiming to capture a complex external world that almost always defies simple description or measure.

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Not even the logical core at the heart of science can escape “artistic” thinking.

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There are more and less obvious ways in which the scientific process is like art. Albert Einstein, for example, seems like a paradigmatic case of the creative scientist, thinking beyond the constraints of his contemporary physics to innovate new ways to understand the fundamental structures of reality. Marie Curie too was an artist, and so were Katherine Johnson, Charles Darwin, Leonardo Da Vinci, Thābit ibn Qurra, and Ada Lovelace. They thought creatively about the world around them, founding entire new fields of research, or changing the course of science’s history. But creativity is also evident in the work of every scientist who has ever had to ponder a novel problem and figure out a solution. Beyond theoretical innovation,  we must remember, that science also encompasses the myriad of measurements, experiments, technologies, principles, and narratives, all of which were designed and built by people thinking not algorithmically, like machines, but thinking creatively, like artists.

The rabbit hole goes deeper: Not even the logical core at the heart of science can escape “artistic” thinking. When we ask ourselves what distinguishes science from other fields of knowledge three answers often float to the top: that science is objective, rigorous, and it follows the scientific method. If there were any candidates for the structures which might make science an exact, rigid field of study, in contrast to the “airy” judgments of art, it is these. So let’s look at them a little closer. Consider ‘objectivity’, a phrase which conjures up ideas of the emotionally devoid and detached scientist in the lab, carefully distanced from their field of study to avoid skewing data or results. What does it really mean when we entreat scientists to be objective though? Most generally, we are asking them to avoid bias, to be thorough, to avoid jumping to conclusions. But more specifically, we’re asking each scientist to think about what ‘objectivity’ means for their particular project, with its particular goals, and in its particular context. To be objective as an oceanographer studying global warming is wildly different to the objectivity of NASA researchers designing new types of material for use in space shuttles, and it is different in turn from the objectivity of a psychologist studying patients.

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There is no clear answer to what constitutes “rigorous” science, nor “the scientific method”, each of these concepts needs to be decided in situ, by the scientists themselves, via nuanced judgement about what best suits their goals and situation.

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There are similarities between these things, of course, but not the kind where we can simply plug and play, where we can point to a list of actions which cover all of these scientists equally. Instead, figuring out what objectivity means is an exercise in careful judgement, constrained by specialised knowledge of the context they’re working in, and what their particular goals are. Scientists are like artists asked to depict a horse, who must then figure out what angle to depict the horse from, what sort of materialsand techniques would best approximate the aspects of the horse they want to depict? How big does the horse need to be to have the desired impact? Similarly in science, there are judgements to be made when conducting research.  Is it better to do this study blind or double blind? How much should the research subjects know about the aims of the study? What counts as a systematic survey of the local seagrass growth? How is “healthy” defined in the context of nutrition?

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Similarly there is no clear answer to what constitutes “rigorous” science, nor “the scientific method”, each of these concepts needs to be decided in situ, by the scientists themselves, via nuanced judgement about what best suits their goals and situation. Which is not to say that there are no constraints on these concepts, there are clear guidelines for what kind of thing a rigorous, objective, project that follows the scientific method might be. But to claim that these things constitute a mechanical algorithm, with no creative input and judgements on the part of their users, is to ignore the way scientists actually work in practice.

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There is not, and can never be, a clear mechanical, objective, rigorous, scientific process that captures the nuances of how all of the pieces interact in science.

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Science requires so much creative thinking in part because it is so complex – it consists not just of theories and hypotheses, but all the experiments, data, methodologies, techniques, practices, devices, measures, classification schemes and so forth that science also produces and uses - a tangled network of interacting pieces that must be carefully manipulated and constructed to achieve set goals. There is not, and can never be, a clear mechanical, objective, rigorous, scientific process that captures the nuances of how these pieces interact in science, nor the expert judgements that scientists make on a regular basis as they use these pieces together to generate new scientific knowledge and products. All of this is made even more difficult by the fact that the world itself is a messy and complicated place, and in trying to explain it we are trying to imitate a world that has hidden the rules deep. Nature is, in the words of Nancy Cartwright “an artful modeller [1]" and so to capture it, so too must be our scientists.

To extend the analogy, scientists are like artists trying to recreate a still-life scene. Artists work carefully, with constant reference back to the scene in front of them, guided by highly-trained intuition and judgement, and by knowing what worked for other artists and what failed. They look at the work of their peers, they read descriptions of how others proceeded, and try their best to borrow the good bits, and create new approaches when old ones don’t fit their goals. So do the scientists. 

Science is rigorous, objective, and follows the scientific method. But science is also, fundamentally, creative.