Oh what a mess!

Oh what a mess!

I’ve struggled writing this blog post: (1) I’ve never written a blog post before but always imagined my first blog post to be edgy, witty, or ending with a picture of me raising a champagne flute into the air with the sun setting in the background as I dig my toes into the luxurious sand of some tropical island that’s the perfect mix of exotic, difficult to pronounce, yet still elicits an envious response from all of my followers, (2) bias and irreproducibility are both distinct and related, difficult to separate and examine independently, (3) concerning the “reproducibility crisis” well even that’s being debated.  

In a Nature News Feature article, 1,576 researchers were asked a simple question: “is there a reproducibility crisis?” 52% of those surveyed said “Yes, a significant crisis,” while 38% said “Yes, a slight crisis,” 3% answered that there was no crisis and 7% said they didn’t know.

When broken down into factors that contribute to irreproducible research, the Nature news article concludes that top related factors relate to intense competition and time pressure… umm let me check the honor code really quickly… yeah, as I expected there’s no clause excusing cheating because of impending deadlines or pressure to get a better grade. So why do fool ourselves into thinking that dishonesty motivated by intense competition and time pressure might be acceptable? Naming contributing factors like selective reporting, pressure to publish, low statistical power or poor analysis, and not replicating enough in original lab.

Tying this into Dan Ariely’s video, “The Honest Truth About Dishonesty”, perhaps the real crisis lies in the lies and biases we hide behind, things like “everybody is doing it” or there’s no identifiable victim. Dr. Ariely suggests that exercises like signing an honor code primes people with their own morality and decreases incidences of cheating, maybe priming people with their own competence, asking them to verify they’ve received appropriate mentoring, developed the proper methods or code, and designed the best experiment could work to decrease incidences that lead to irreproducibility.  

Why, Why, Tell 'em That It's Human Nature

Humans—the creators of the seven world wonders and the source of global warming. We are a perfectly imperfect whether we know it or not and, as humans, scientists are not immune to this fact. The simple human nature of scientists arises in many forms. For example, human error appears during the experiment implementation process, while human desire makes its appearance as we all long to make a discovery that would positively impact our field. Lastly, our natural survival instincts and competitive air settles in as we feel the pressure to produce quality data in order to secure funding.

Basic human behavior is simple to display in the sciences, but how do we ensure the line between human nature and scientific ethics remains bold and defined? Neil DeGrasse Tyson stated in a short Q&A response that scientists are ”trained to minimize the role of our bias in our experiments and interpretations” but in all actuality, it’s impossible to completely remove ourselves from the equation. 

To help with this issue, Tyson points out the fact that the scientific process has a verification step built into it in which our peers hold us accountable to our duty to remain objective and driven by the facts. Lucky us to have a system that is ready to catch us if we fall, but what price is there to pay if we choose this option? What happens to us scientists if we choose to rely on the scientific process to keep us honest or if our human traits of error, desire, competition, and survival lead us to the wrong “truth” enabling us to see what we want to see in our experimental setup or data? Oh, well, only the possibility of discounting our future work in the eyes of our colleagues… clearly there’s got to be a better way than allowing “the system” to catch and discipline us. The answer sounds very Disney-ish: it all starts with you.

We as HUMAN scientists are able to police ourselves into remaining objective in the name of science. Our first steps of doing this lies in the selection of a testable (and refutable) hypothesis and the ethical choice to note experimental conditions or results that might make the resulting data invalid. While our bias is inherent to us as human beings, luckily we  have ourselves and “the system” to keep us in line.