Sunday, January 17, 2016

The bias of priming and replication experiments

           In the TED talk given by Dan Ariely, (Source), he describes several experiments where he tests individual circumstances to test the frequency of cheating among college students. The one that struck me most was where individuals were given twenty math problems to complete in an insufficient amount of time. One member of the crowd was a hired actor, who stood up quickly, announced that they had finished the problems, thus providing an example of someone who appears to have cheated. The results Ariely found were that people in the crowd were more likely to cheat if the actor had a sweatshirt from their university.  Providing the idea that the frequency of cheating is based on our concept of our “in-group”. Ariely says, “if someone from our in-group cheats, we see them cheating, we feel it is more appropriate as a group to behave this way” (Source). This concept reminded me of the idea of priming from the Trouble in the Lab article from The Economist (Source)
            Priming studies are based around the idea that  “decisions can be influenced by apparently irrelevant actions or events that took place just before the cusp of the choice” (Source). In Ariely’s experiment, the actor was that irrelevant event, and the response his actions elicited was based on whether people perceived the actor as one of their own. I couldn’t help but question why this occurs. In a scientific context, where graduate student and mentor conduct experiments, it seems understandable that the actions of one person (the mentor) may set a benchmark for the other (the graduate student). Regardless of whether that standard is negative and conducive to cheating, or positive and a high ethical stance, we as student scientists cannot deny the impact of mentors on our individual approaches to science and data. As a current rotation student, I thought my personal approach to this was decently defined, and hadn’t given much thought to how my decisions are influenced by potential mentors or even lab-mates. Now, I could say I’m in the very least more aware of how others in the lab group impact my approach to science overall.
            Along the same thought of the impact of others on an individual’s actions, Trouble in the Lab also raised the issue of how most published research findings are flawed in one of three ways. The most interesting to me was, “the pervasive bias favoring the publication of claims to have found something new” (Source). This ‘pressure to publish’ concept seems to be the primary motivation factor for much research, and is disenchanting for me. The concept of novel, groundbreaking discovery is always exciting in science, but I don’t feel it should be the target goal. Setting the target so loftily seems to create an environment where this “publish or perish” concept has arisen from, and it primes scientists to chase game-changing discoveries at the sacrifice of personal and scientific standards. An over-arching theme of both articles by J. Belluz (Source 1, Source 2), was how many miraculous cancer drugs lack the significant data to back their claims, as shown by the replications of the original studies.

In regards to reproducibility in research, I can easily see the need for replication studies, but from a funding and career standpoint, I can also agree against them. Not only would it be extremely difficult to get funding for a replicate study, but, as Trouble in the Lab phrased it, “Most academic researchers would rather spend time on work that is more likely to enhance their careers” (Source). I do still agree that, “the community should find effective mechanisms for sharing the results of replication experiments, both successful and unsuccessful” (Source), but my agreement stems more from a desire to have more acceptances of negative/unsuccessful data in science communications that there currently is, rather than to have more communication of replication experiments as a whole. Additionally, the problem with replication experiments presented by Trouble in the Lab that, “only people with an axe to grind pursue replications with vigour” (Source), introduces a problematic sense of bias to the replicated work and thus, at least from my perspective, diminishes the intent behind the replication.
The inciting incident of the actor in Ariely’s experiment primed the group to feel at ease with cheating, and thus increased the frequency that cheating occurred. Overall, I feel these articles have provided a sense of individual ethical standards, where a sense of alertness is necessary, in regards to how the behaviors and choices of those around us impact our own biases. In our cases, these would be our fellow scientists and lab mates in regards to how they conduct experiments, or handle, present, and manipulate data, and how those decisions thus color our individual practices and the bias we apply to them. The quote from these articles that best encapsulates my feelings in regards to priming and bias in science is, “each researcher has a responsibility to ensure that his or her own published work is as reliable as possible within the limits imposed by resources and other constraints” (Source).

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