The Path of Scientific Inquiry

01/31/2011 By Shawn Burns

We have a funny kind of relationship with scientific inquiry. On the one hand we are very, very casual about accepting the products of scientific progress. We wear engineered clothing and drive cars (sadly, not flying ones), fly in air planes, watch the Mythbusters on the magic box on the living room that isn’t even a box any more so much as a magic window on the wall, play electric guitars, navigate to the nearest church with our GPS-enabled phones, wearing our butt-firming tennis shoes and listening to NPR on our radios.

On the other hand, we say some pretty radical things about scientific pursuits that make the news instead of hit the shelves, and we accuse scientists of being in everyone’s pocket or having hidden agenda.

Science is a process, and a philosophy about interacting with the world around us. The process says “Begin with something requiring explanation. Hypothesize. Design experiments to falsify the hypothesis. Theorize.” The philosophy says “Truth is approximated through the process.”

Built into the process, and thus to the philosophy, is the idea that scientific inquiry is in part retrograde, walking backward a little in order to walk forward. One of the key elements of arriving at the approximated truth is the attempt to show that the hypothesis, the theory explaining a phenomenon, is false. Weak theories die their deaths early on. A weak theory is one that fails to explain the phenomena it sought to explain. Sometimes a weak theory survives for a little while because the method of falsification, of testing it, is compromised or ill-considered. But for the most part these flaws are also pointed out early on, and the theory dies. Weak theories die. Strong theories survive.

Strong theories are not perfect. They explain the phenomena better than competing theories, but they aren’t “true” except in a pragmatic sense of “true” that means “useful for predicting the future”. Even the strongest theory can encounter unusual phenomena that it has trouble accounting for.

But unlike a weak theory, which dies when faced with unexplained phenomena, a strong theory can be adapted, its key elements left in place, while it evolves to include explanations for new phenomena.

It is the nature of theories, strong and weak, to be challenged. However, it is not in the nature of scientific inquiry to be regressive. Regressing to earlier theories to explain phenomena because a current theory is faced with problematic data happens almost exclusively in the early experimentation days of a theory. It doesn’t happen to a tested theory. Tested theories, surviving the infancy of experimentation, will adapt, and progress, rather than revert to an earlier version. The earlier version failed because it couldn’t explain the phenomena it had to, even then. It is not worth returning to.

Our constant challenging of theories, in order to test them, is cognitively at odds with our acceptance of the progress of scientific inquiry: we have to accept both that science is generally right, in trajectory, while also accepting that most scientific theories are wrong. Most scientific theories are wrong and never get beyond the testing stage. The evidence of scientific inquiry is that if we wait for a little while after hearing of a new theory we will discover falsifiying evidence and the theory will be abandoned or improved. But this leads us to distrust scientific inquiry in general: How can we trust what scientists tell us when by and large scientific theories are wrong?

The reason we ought to trust what scientists tell us is because science is generally right, even though it is also generally wrong. The theories that survive the infancy of early experimentation tend to be adaptable, strong theories, instead of falsifiable, weak theories. Theories that survive the early experimentation days cannot be killed with a single bullet. They cannot be challenged on the same level as the theories that don’t survive the early stages.*

But this doesn’t stop people from trying. They look for magic bullets, single instances or problems that the theory hasn’t coped with yet, and pronounce the theory not only false for failing to account for the phenomenon, but often also take that limited, or imagined, failure as evidence of total theory collapse.

This is how the arguments against evolution, vaccines, and global warming often progress. They take a built-in element of scientific inquiry, the falsifiability of theories, and use that as a very specific complaint about the theory in question: How can this theory be true if theories are generally wrong?

Although often offered as “We need more testing” arguments, that’s not the real goal. The real goal is to replace the theory not with an alternate, better theory that is also a progression along the scientific inquiry trajectory, but to regress, to undermine the validity of the trajectory itself. “We shouldn’t trust science to provide us with anything approximating truth because the global warming theory doesn’t explain why this winter was so much colder in Alabama!” or “We ought to reject the theory of evolution because it’s so simple and life is so complex, so let’s replace it with a metaphysical unlikelihood!” or “We ought to cease vaccinating children against diseases because the theories that explain how vaccines react with the body and with pandemics don’t explain why so many children are on the autism spectrum now!” These are regressions of strong theories, not adaptations of them in the face of evidence. And in many cases what the theories are being asked to account for are actually already accounted for, but learning this requires sitting through a tedious explanation by someone who has studied the problem for years; Wikipedia gives us the illusion of intellectual equality and instant expertise, and we value our own judgments so highly that we’ll reject the trajectory of scientific inquiry before considering that we may not be qualified to participate in the conversation. Science is not a democracy, it’s a meritocracy.

Nine times out of ten a strong theory will predict the right outcomes. For some, a success rate like that is enough to think the general direction is the right one. For others, a success rate like that is somehow enough to think that the opposite direction is the right one, that all of the conclusions scientists make are suspect and should be rejected on principle.

The problem with that belief is that it is not only regressive, but holds scientific inquiry up to a standard of truth that it never pretended to have. Science tells us what happened, and attempts to tell us why it happened, so that we can also know what will happen. It cannot promise to really tell us anything other than what happened. The why is a theory, and the what will is a guess. The 1/10 critic expects that the best scientific theory will not just guess, but promise with absolute certainty, or else the process isn’t worth pursuing.

Having undermined the process with extreme goals, the 1/10 critic has undermined the trajectory: how can we be sure that scientific inquiry is progressing at all when the best it can do is guess about the future? From there it is a short step to undermining individual theories: Once you are convinced the direction is suspect, individual theories gain no value from having survived the testing stage; even these strong theories can be rejected with magic bullet arguments, because they no longer demonstrate any legitimacy in virtue of having survived the early testing stages. The 1/10 critic rejects even established theories using the magic bullet arguments that are usually only valid against early theories. This critic does not expect the theory to adapt, but to die. All because he holds a standard of truth in science that no theory meets.

So, doubt the claims of scientists if you want to. Undermine them with human complaints about agendas and payola and political bents. But just like calling God a fascist won’t change the colour of the sky, calling a scientific theory “liberal” or “Big Pharma” won’t change the job the scientist has in front of him: to theorize from data in order to provide us with reliable predictions about the future.

Now, shh. Mythbusters is on.

(Someone might think that I’m ignoring Thomas Kuhn’s “The Structure of Scientific Revolutions” when I write here about trajectories instead of paradigm shifts. But Kuhn was talking about revolutions. I think what’s at stake here is what he would have referred to as “normal science” instead of “revolutionary science”.)