The Isolation of Experimental Variables

Simplifying assumptions in "hard" sciences vs real-world deployment.

The isolation of a very few variables-ideally just two, while controlling all others-is a key tenet of experimental science.74 As a procedure, it is both valuable and necessary to scientific work. Only by radically simplifying the experimental situation is it possible to guarantee unambiguous, verifiable, impersonal, and universal results .71 As a pioneer in chaos theory has put it: "There is a fundamental presumption in physics that the way you understand the world is that you keep isolating its ingredients until you understand the stuff you think is truly fundamental. Then you presume that the other things you don't understand are details. The assumption is that there are a small number of principles that you can discern by looking at things in their pure state. This is the truly analytic notion-and somehow you put these together in some more complicated ways when you want to solve more dirty problems. If you can."16 In agricultural research, controlling for all possible variables except those under experimental scrutiny required normalizing assumptions about such things as weather, soils, and landscapes, not to mention normalizing assumptions, often implicit, about farm size, labor availability, and the desires of cultivators. "Test-tube research," of course, most closely approximated the ideal of controls.77 Even the experimental plot on a research station, however, was itself a radical simplification. It maximized the degree of control "within a small and highly simplified enclosure" and ignored the rest, leaving it "totally out of control."78

It is easy to see how monoculture and attention to quantitative yields would fit most comfortably within this paradigm. Monoculture eliminates all other cultivars that might complicate the design, while concern with quantitative yields avoids the thorny measurement problems that would arise if a particular quality or taste were the objective. The science of forestry is easiest when one is interested only in the commercial wood from a single species of tree. The science of agriculture is easiest when it is a question of the most efficient way of getting as many bushels as possible of one hybrid of maize from a "normalized" acre.


To the extent that science is obliged to deal simultaneously with the complex interactions of many variables, it begins to lose the very characteristics that distinguish it as modern science. Nor does the accumulation of many narrow experimental studies add up to the same thing as a single study of such complexity. This is not, I must repeat, a case against the experimental techniques of modern scientific research. Any extensive, on-farm research study that did not reduce the complexity of interactions might be able to show, as farmers can, that a set of practices produced "good results": say, high yields. But it would not be able to isolate the key factors responsible for this result. The case that I am making instead recognizes the power and utility of scientific work, within its domain, and recognizes its limitations in dealing with the kinds of problems for which its techniques are ill suited.

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seeing-like-a-state Scott, James C. 1998. Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed. Yale University Press. ↩︎ 1