Why Climate Scientists Should Not Be Trusted

  • Date: 01/01/10

Wired Magazine: Kevin Dunbar is a researcher who studies how scientists study things — how they fail and succeed. In the early 1990s, he began an unprecedented research project: observing four biochemistry labs at Stanford University.

Philosophers have long theorized about how science happens, but Dunbar wanted to get beyond theory. He wasn’t satisfied with abstract models of the scientific method — that seven-step process we teach schoolkids before the science fair — or the dogmatic faith scientists place in logic and objectivity. Dunbar knew that scientists often don’t think the way the textbooks say they are supposed to. He suspected that all those philosophers of science — from Aristotle to Karl Popper — had missed something important about what goes on in the lab. (As Richard Feynman famously quipped, “Philosophy of science is about as useful to scientists as ornithology is to birds.”) So Dunbar decided to launch an “in vivo” investigation, attempting to learn from the messiness of real experiments.

He ended up spending the next year staring at postdocs and test tubes: The researchers were his flock, and he was the ornithologist. Dunbar brought tape recorders into meeting rooms and loitered in the hallway; he read grant proposals and the rough drafts of papers; he peeked at notebooks, attended lab meetings, and videotaped interview after interview. He spent four years analyzing the data. “I’m not sure I appreciated what I was getting myself into,” Dunbar says. “I asked for complete access, and I got it. But there was just so much to keep track of.”

Dunbar came away from his in vivo studies with an unsettling insight: Science is a deeply frustrating pursuit. Although the researchers were mostly using established techniques, more than 50 percent of their data was unexpected. (In some labs, the figure exceeded 75 percent.) “The scientists had these elaborate theories about what was supposed to happen,” Dunbar says. “But the results kept contradicting their theories. It wasn’t uncommon for someone to spend a month on a project and then just discard all their data because the data didn’t make sense.” Perhaps they hoped to see a specific protein but it wasn’t there. Or maybe their DNA sample showed the presence of an aberrant gene. The details always changed, but the story remained the same: The scientists were looking for X, but they found Y.

Dunbar was fascinated by these statistics. The scientific process, after all, is supposed to be an orderly pursuit of the truth, full of elegant hypotheses and control variables. (Twentieth-century science philosopher Thomas Kuhn, for instance, defined normal science as the kind of research in which “everything but the most esoteric detail of the result is known in advance.”) However, when experiments were observed up close — and Dunbar interviewed the scientists about even the most trifling details — this idealized version of the lab fell apart, replaced by an endless supply of disappointing surprises. There were models that didn’t work and data that couldn’t be replicated and simple studies riddled with anomalies. “These weren’t sloppy people,” Dunbar says. “They were working in some of the finest labs in the world. But experiments rarely tell us what we think they’re going to tell us. That’s the dirty secret of science.”

How did the researchers cope with all this unexpected data? How did they deal with so much failure? Dunbar realized that the vast majority of people in the lab followed the same basic strategy. First, they would blame the method. The surprising finding was classified as a mere mistake; perhaps a machine malfunctioned or an enzyme had gone stale. “The scientists were trying to explain away what they didn’t understand,” Dunbar says. “It’s as if they didn’t want to believe it.”

The experiment would then be carefully repeated. Sometimes, the weird blip would disappear, in which case the problem was solved. But the weirdness usually remained, an anomaly that wouldn’t go away.

This is when things get interesting. According to Dunbar, even after scientists had generated their “error” multiple times — it was a consistent inconsistency — they might fail to follow it up. “Given the amount of unexpected data in science, it’s just not feasible to pursue everything,” Dunbar says. “People have to pick and choose what’s interesting and what’s not, but they often choose badly.” And so the result was tossed aside, filed in a quickly forgotten notebook. The scientists had discovered a new fact, but they called it a failure.

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