Note from Jeremy: this is a guest post from Greg Crowther. Greg has a Ph.D. in biology and has held several teaching and research positions at the University of Washington and other Seattle-area colleges. He’s currently working on a master’s in science education.
I’ve never been inordinately curious about the natural world. As a kid, I did not spend long hours using a telescope or a home chemistry set, nor did I catch frogs in marshes or learn to identify species of local flora. I got to high school, and then college, without any clear sense that I should become a scientist or that I would enjoy this particular vocation.
In my first four semesters of college, I took the usual variety of courses and grappled with their many fascinating questions. Why did the Vietnam War start? What do Buddhists really believe? How did E.M. Forster’s novel Howards End illustrate his directive of “Only connect”?
Though fascinating, these questions also seemed horribly intractable. One could cite evidence from a primary or secondary source to support one interpretation or another, but there didn’t seem to be any standard way of resolving disagreements besides deferring to the authority of the professor.
Science was different, though. Professors presented the so-called “scientific method” as a fair, objective way of evaluating the strength of different possible explanations. Accrue some background knowledge via reading and observation; pose a hypothesis; design an experiment to test the hypothesis; determine whether the data collected are consistent with the predictions of the hypothesis; and discard, modify, or retain the hypothesis as appropriate.
It all sounded so orderly, so sensible, so feasible. Even if I did not have a great big hypothesis of my own, I could imagine taking someone else’s hypothesis out for a spin, say, using a species that hadn’t been studied yet. This “scientific method” seemed simple enough for novices like me to follow, yet powerful enough to reveal fundamental insights about the world. I was hooked – not on any particular molecule or technique or theory, but on the logical flow of the process itself. I’ve considered myself a scientist ever since, and I now present the scientific method (often called the process of science) to my own students – because it’s relevant to their futures (whether or not they become scientists), but under-taught and poorly understood – more or less as it was presented to me.
“But wait!” cry various smart, articulate people such as Terry McGlynn and Brian McGill. “That’s not how scientific research really works!” Indeed, UC-Berkeley has an entire website, How Science Works, devoted to debunking and revising what it calls the “simplified linear scientific method.”
How Science Works has four principal objections to the “simplified linear” (SL) model:
The simplified, linear scientific method implies that scientific studies follow an unvarying, linear recipe.
But in reality, in their work, scientists engage in many different activities in many different sequences. Scientific investigations often involve repeating the same steps many times to account for new information and ideas.
The simplified, linear scientific method implies that science is done by individual scientists working through these steps in isolation.
But in reality, science depends on interactions within the scientific community. Different parts of the process of science may be carried out by different people at different times.
The simplified, linear scientific method implies that science has little room for creativity.
But in reality, the process of science is exciting, dynamic, and unpredictable. Science relies on creative people thinking outside the box!
The simplified, linear scientific method implies that science concludes.
But in reality, scientific conclusions are always revisable if warranted by the evidence. Scientific investigations are often ongoing, raising new questions even as old ones are answered.
To capture the complexity of science, including its iterative, social nature and its connections with society and technology, UC-Berkeley replaces the SL model with a sprawling four-panel illustration of How Science Works (HSW). The illustration includes four interacting circles – Exploration & Discovery, Testing Ideas, Community Analysis & Feedback, and Benefits & Outcomes – each of which can be expanded and explored in more detail.
Well, it depends on one’s pedagogical goals, of course.
If your main goal is to capture the complexity of how science is practiced in the real world, then HSW is the model for you.
But what if you want to inspire confidence that science can provide solid answers to fundamental questions? Which model more clearly conveys a sense of progress? Or what if you wish to emphasize that truly scientific theories can be falsified (a la Karl Popper), or that good scientific theories lead to startling predictions of novel facts (a la Imre Lakatos), and you want to focus attention on hypothesis testing? I vote for SL.
Yet another way to portray the process of science is exemplified by the Next-Generation Science Standards (NGSS) for K-12 education. The NGSS share some of UC-Berkeley’s concerns about the SL model:
Students are told that there is “a scientific method,” typically presented as a fixed linear sequence of steps that students apply in a superficial or scripted way. This approach often obscures or distorts the processes of inquiry as they are practiced by scientists.
NGSS’s solution is to present a set of eight key Science and Engineering Practices, as follows: ask questions; develop and use models; plan and conduct investigations; use and interpret data; use math and computational thinking; construct explanations; argue from evidence; and obtain, evaluate, and communicate information.
It’s a nice, compact set capturing the important things that professional scientists really do. As with the HSW model, though, the NGSS model’s admirable realism obscures any clear sense of how scientific progress is made. The Practices are not presented as a linear sequence because, in the real world, they are not executed in one particular order. Fair enough, but, as a result, there is no obvious thread of how hypotheses get formulated, get tested, and get rejected or retained.
My preference to keep “linear” hypothesis testing in the foreground is hardly unique. While it is easy to dismiss this linear path as idealized or unrealistic, its real-world value is underscored by the movement toward preregistration of experiments, for example. One can also ask why we often report on studies as if they were conducted to test hypotheses that were actually thought of afterward (“HARKing”). It seems that a lot of professional scientists want science to work this way, even if reality does not always cooperate.
I acknowledge the limitations of the SL model, of course. As a graduate student and as a postdoc, I experienced plenty of nonlinearity and irrationality in my own work. The old Albert Einstein quote comes to mind: “If we knew what we were doing, it wouldn’t be called research.” And, as noted by both How Science Works and NGSS, the SL model can certainly be applied in superficial, unhelpful ways. For my 10-year-old son’s science fair project last winter, he included a hypothesis and a prediction, as required by the rubric, but the two were unrelated! Still, I believe that the SL model itself is “wrong” mainly in the sense that all models are simplifications and therefore incomplete at best. I don’t see how this particular simplification of reality is any more misleading or egregious than any other textbook-level explanation we offer to our students. On the contrary, I see it as a wonderful unifying framework that represents the scientific enterprise at its best. Thus, my preference and my recommendation is to mostly teach from the SL model. But, as always, I’m looking forward to your comments.