“Science is an ongoing race between our inventing ways to fool ourselves, and our inventing ways to avoid fooling ourselves.” – Saul Perlmutter
Nature has published an interesting article: How scientists fool themselves, and how they can stop, by Regina Nuzzio. Excerpts:
This is the big problem in science that no one is talking about: even an honest person is a master of self-deception. Our brains evolved long ago on the African savannah, where jumping to plausible conclusions about the location of ripe fruit or the presence of a predator was a matter of survival. But a smart strategy for evading lions does not necessarily translate well to a modern laboratory, where tenure may be riding on the analysis of terabytes of multidimensional data. In today’s environment, our talent for jumping to conclusions makes it all too easy to find false patterns in randomness, to ignore alternative explanations for a result or to accept ‘reasonable’ outcomes without question — that is, to ceaselessly lead ourselves astray without realizing it.
“People forget that when we talk about the scientific method, we don’t mean a finished product,” says Saul Perlmutter, an astrophysicist at the University of California, Berkeley. “Science is an ongoing race between our inventing ways to fool ourselves, and our inventing ways to avoid fooling ourselves.” So researchers are trying a variety of creative ways to debias data analysis — strategies that involve collaborating with academic rivals, getting papers accepted before the study has even been started and working with strategically faked data.
“As a researcher, I’m not trying to produce misleading results,” says Nosek. “But I do have a stake in the outcome.” And that gives the mind excellent motivation to find what it is primed to find.
One trap that awaits during the early stages of research is what might be called hypothesis myopia: investigators fixate on collecting evidence to support just one hypothesis; neglect to look for evidence against it; and fail to consider other explanations. “People tend to ask questions that give ‘yes’ answers if their favoured hypothesis is true,” says Jonathan Baron, a psychologist at the University of Pennsylvania in Philadelphia. By focusing on one hypothesis, researchers might be missing the real story entirely.
The Texas sharpshooter
A cognitive trap that awaits during data analysis is illustrated by the fable of the Texas sharpshooter: an inept marksman who fires a random pattern of bullets at the side of a barn, draws a target around the biggest clump of bullet holes, and points proudly at his success.
“You just get some encouragement from the data and then think, well, this is the path to go down,” says Pashler. “You don’t realize you had 27 different options and you picked the one that gave you the most agreeable or interesting results, and now you’re engaged in something that’s not at all an unbiased representation of the data.”
The data-checking phase holds another trap: asymmetric attention to detail. Sometimes known as disconfirmation bias, this happens when we give expected results a relatively free pass, but we rigorously check non-intuitive results. “When the data don’t seem to match previous estimates, you think, ‘Oh, boy! Did I make a mistake?’” MacCoun says. “We don’t realize that probably we would have needed corrections in the other situation as well.”
As data-analysis results are being compiled and interpreted, researchers often fall prey to just-so storytelling — a fallacy named after the Rudyard Kipling tales that give whimsical explanations for things such as how the leopard got its spots. The problem is that post-hoc stories can be concocted to justify anything and everything — and so end up truly explaining nothing.
Another temptation is to rationalize why results should have come up a certain way but did not — what might be called JARKing, or justifying after results are known.
In every one of these traps, cognitive biases are hitting the accelerator of science: the process of spotting potentially important scientific relationships. Countering those biases comes down to strengthening the ‘brake’: the ability to slow down, be sceptical of findings and eliminate false positives and dead ends.
One solution that is piquing interest revives an old tradition: explicitly considering competing hypotheses, and if possible working to develop experiments that can distinguish between them. This approach, called strong inference, attacks hypothesis myopia head on. Furthermore, when scientists make themselves explicitly list alternative explanations for their observations, they can reduce their tendency to tell just-so stories.
Another solution that has been gaining traction is open science. Under this philosophy, researchers share their methods, data, computer code and results in central repositories, such as the Center for Open Science’s Open Science Framework, where they can choose to make various parts of the project subject to outside scrutiny. Normally, explains Nosek, “I have enormous flexibility in how I analyse my data and what I choose to report. This creates a conflict of interest. The only way to avoid this is for me to tie my hands in advance. Precommitment to my analysis and reporting plan mitigates the influence of these cognitive biases.”
Team of rivals
When it comes to replications and controversial topics, a good debiasing approach is to bypass the typical academic back-and-forth and instead invite your academic rivals to work with you. An adversarial collaboration has many advantages over a conventional one, says Daniel Kahneman, a psychologist at Princeton University in New Jersey. “You need to assume you’re not going to change anyone’s mind completely,” he says. “But you can turn that into an interesting argument and intelligent conversation that people can listen to and evaluate.” With competing hypotheses and theories in play, he says, the rivals will quickly spot flaws such as hypothesis myopia, asymmetric attention or just-so storytelling, and cancel them out with similar slants favouring the other side.
It is often difficult to get researchers whose original work is under scrutiny to agree to this kind of adversarial collaboration, he says. The invitation is “about as attractive as putting one’s head on a guillotine — there is everything to lose and not much to gain”. But the group that he worked with was eager to get to the truth, he says. In the end, the results were not replicated. The sceptics remained sceptical, and the proponents were not convinced by a single failure to replicate. Yet this was no stalemate. “Although our adversarial collaboration has not resolved the debate,” the researchers wrote, “it has generated new testable ideas and has brought the two parties slightly closer.”
Blind data analysis
One debiasing procedure has a solid history in physics but is little known in other fields: blind data analysis. The idea is that researchers who do not know how close they are to desired results will be less likely to find what they are unconsciously looking for.
One way to do this is to write a program that creates alternative data sets by, for example, adding random noise or a hidden offset, moving participants to different experimental groups or hiding demographic categories. Researchers handle the fake data set as usual — cleaning the data, handling outliers, running analyses — while the computer faithfully applies all of their actions to the real data. They might even write up the results. But at no point do the researchers know whether their results are scientific treasures or detritus. Only at the end do they lift the blind and see their true results — after which, any further fiddling with the analysis would be obvious cheating.
Nature has another article Blind analysis: hide results to seek the truth that provides discussion about this.
This general topic is one that has been a frequent topic of discussion at CE:
- The method of multiple working hypotheses
- Mutually assured delusion
- Science is not about certainty
- False(?) positives
- On torturing data
The climate science field is frequently guilty especially of
- hypothesis myopia
- just-so storytelling
My favorite ways of trying to avoid such biases is multiple working hypotheses, and the related teams of rivals. These strategies are antithetical to manufacturing consensus and attempting to marginalize (or RICO-ize) those that disagree with you.