If you’ve ever taken a basic science class, you‘ve probably heard of the scientific method. Maybe you’ve even designed, conducted, and reflected upon your own experiment–inside or outside of the classroom. One thing you may be unfamiliar with, however, is how the millions of experiments done by scientists, students, and citizens get added together into a coherent body of knowledge on a topic. In other words, how does science actually progress?
With ‘science’ at the forefront of many contemporary debates, science and data literacy are hot topics. There’s even a body of scientific research on science literacy itself, which shows how we all have preconceived ideas and biases which can affect our interpretations of scientific data. Missing from this recent literature, I think, is a basic description of how scientists, as a community, come to a consensus on facts, laws, and other important aspects of work in their field. Furthermore–what happens when scientists don’t agree about some aspect of their research? How are scientific debates different than those in liberal arts, philosophy, and everyday life?
Enter philosopher of science Thomas Kuhn–who published one of the most influential books on science in the 20th century. I read The Structure of Scientific Revolutions (1962) during my first week of graduate school, and although some disagree with Kuhn’s characterization of scientific progress, I think his basic premise explains how science works and progresses over generations really well. Kuhn’s conception has four main phases:
1) Immature science: a field where very little is known about the topic, and thus no research-driving theoretical framework exists, only simple observation and examination of phenomena.
Think space exploration before the Cold War, the interpretation of fossils before the knowledge of geological time scale, and heredity research before the discovery of DNA.
2) Normal Science–where researchers use the tools and technologies of the day to collect data and shed light on some question about their field. This is the scientific method (above) in action.
Normal Science typically has two main features: First, a paradigm, which is a unified theoretical basis for designing more experiments and collecting new data. Second, an accumulation of inconvenient data, resulting from the very experiments meant to answer questions (not complicate them!). These anomalous findings are very important, because they lead to…
3) Crisis–where the paradigm (the agreed-upon theoretical and practical basis for the science) is no longer supported by all of the data. This stage is perhaps the most important aspect of Kuhn’s work, because it addresses the above question: how do scientific disputes get resolved?
For good examples of contemporary science in various modes of crisis: look no further than countless studies on modern diets, the replication crises facing medicine and psychology, the best way to design and train Artificial Intelligence, etc.
What these crises have in common is that, when one occurs, researchers begin designing and conducting experiments explicitly to resolve it: they try to collect data which will either refute the existing anomalies, or explain them away. This stage, of “competitive experimenting,” if you will, is where scientific debate differs from other forms of argumentation, because while the scientists may have ideas or theoretical preferences about the paradigm in question, they must rely upon the data–the entire body of relevant data–in order to move forward. Whether the current paradigm survives or is refuted depends on the data too (not just on the people who collect it!), and scientists must eventually accept where that data leads and what it tells us. Data-driven debate often takes decades, unfortunately leaving the floor open for pop science and pseudoscience to fill the gap. But crisis also typically leads to newer, better paradigms, re-invigorating and re-focusing entire disciplines. Thus, the final steps in Kuhn’s schema are:
4) The Paradigm Shift and the Post-Revolution–where the new framework or theory (explaining and supported by both the old and new data) is agreed upon by most in the field. These then begin to drive normal science (2), which may or may not lead to crisis (3), and yet another paradigm (4).
The Structure of Scientific Revolutions might not be your idea of a fun read, so from me–as a young scientist–to you, here are the “take-homes” I hope to convey: many scholastic enterprises use evidence-based argumentation, but because of its dependence on data and paradigms, scientific argumentation can seem incredibly slow, meticulous and opaque. It might help to talk to a scientist or a friend about advances or news in science, but always use sources which place the data at the forefront. Without data, be dubious of scientific claims.
Furthermore, disagreement in science is inherent to (and often necessary for) scientific progress, so if a headline or resource says “scientists disagree…” you shouldn’t be distressed or upset. Instead, try to learn more about the nature of the debate and think about how the data relate to the various positions. If you were one of the scientists, what data would you use to resolve key ambiguities? What kind of experiment could get you that data? Etc.
From the simplest experiments to the most complex climate models, the system outlined above has proven, repeatedly and across many fields, that data-driven approaches to theory building really work. Scientific progress can seem chaotic these days, but that chaos is–as Kuhn suggests–business as usual. Science will continue to progress, which is good news for all of us!