Bias in science

By reviewing a random subset of thoughts that occur to me throughout the day, it is clear to me that the arguments that I make and conclusions that I reach on a regular basis are much more likely to be biased in some way than to be accurate, truthful reflections of the state of the things. This is likely to be caused by both innate biases of the human brain, struggling to build operational frameworks of the world based on limited input while minimizing time and effort (i.e. an evolutionary adaptation), and a certain alignment of incentives in our lives that makes it rewarding enough and/or not too damaging enough for us to warp our interpretations of reality, intentionally or subconsciously, to fit a certain mold (see Dan Ariely’s TED talk [here]).

Nowhere are there higher stakes for recognizing and minimizing bias than in scientific research, which is built on the foundation of seeing the universe as it is, rather than as we want it to be. Still, internal and external biases abound, from large scale publication bias and research “trends”, to reports that unexpectedly high percentages of published research is poorly executed or flat-out wrong (see “Trouble at the Lab” [here]). The incentive structure around science, which funds the grander claims, rewards prolific publication of novel results, and undervalues quality assurance and scientific “due process”, is not helping either. Every time research funding is quickly cut at the first sign of economic uncertainty, every time the media rushes to report unconfirmed results, creating extra unneeded incentives, I can’t help but feel that science is still trying to prove itself to society, to justify the increasing funds coming its way.

The hope of those pondering the fallibility of science, including myself, is that it is a self-correcting process. That amid the chaos, or precisely because of it, the truth will prevail while houses of cards built on wrong hypotheses will come crashing down sooner or later, however high they may rise. Perhaps by slowing the process down a little bit, and investing a little more in the “uncool” science of checking and verifying, we can make science a more efficient and less expensive endeavor.