The Hidden Biases in the Research Industry

Connor Hsuan

Professor Horgan

HST 401

9 February 2026

I pledge my honor that I have abided by the Stevens Honor System.

The Hidden Biases in the Research Industry

While both of the readings from last week greatly surprised me and revealed aspects of the healthcare system I was not aware of, I was most surprised by the article “How to survive the medical misinformation mess”. I was not aware of the level of misinformation that was present within physicians and professionals, which many people completely trust to be accurate. I wanted to investigate the first claim from the article, that published medical research is

not reliable or is of uncertain reliability (Ioannidis 2017). What I found was that scientific findings are often misrepresented in articles and journals due to bias, misreporting of data, and detrimental research practices.

When authors are reporting their findings in publications, many often misrepresent their findings due to their own preconceived biases, whether it's intentional or not. This is known as putting a “spin” on their study findings. A “spin” is defined as specific reporting that fails to faithfully reflect the findings of a study and could influence how the results are perceived to the reader (Boutron). There are several different methods of spin, with each method usually being used along with others. Some of them are misreporting the methods, misreporting the results, misinterpretation, and the selective reporting of outcomes and analysis. 

When reporting on the methods of experiments, some scientists will often beautify their methods with certain phrases. Phrases such as “double blind” or "randomized controlled trial” might make the experiment sound fair, but oftentimes there are huge issues with setup and credibility (Boutron). Similarly, misreporting results can influence reader perception. Common types of misreporting can include ignoring contradictions, showing misleading figures, and selective reporting of statistically significant results. The consequences of these misrepresentations can lead to wasted time and resources, on top of the possibility of improper treatment and diagnoses (Boutron). With all of these possible traps present, how should we minimize the amount of misleading content in scientific publications?

If we want our research to be consistently credible, we will need to start by having other scientists scrutinize research articles more carefully. Scientists should be able to replicate the conditions of experiments and come to the same conclusions as the initial article. This is a process that needs to be undergone by several different teams across the world to ensure no biases or misrepresentations are present. Another aspect that should be addressed is the effect that stakeholders can have when reporting results. What a stakeholder wants can vary greatly, but these expectations do not ensure the highest level of integrity when it comes to reporting results (Ioannidis 2014).  While the presence of stakeholders can be necessary for funding, researchers should acknowledge who these stakeholders are and what kind of effect they may have on their reports. These reports can then be scrutinized by other researchers to see if any bias was truly present. The final aspect that must be addressed are attempts to game the system in an attempt to earn academic titles, grants, and other forms of recognition or power. These forms of “research currencies” do not award those that produce quality research, rather it awards those that are good at moving up the system (Ioannidis 2014). To fix this, we would need a system that incentivizes the recreation of experiments for the sake of validation, possibly with a monetary or status reward. This new system should also be more open to the sharing of resources. Some researchers may be worried at the possibility that competition may “steal” their methods and ideas in an attempt to gain more funding (Ioannidis 2014). This system would need to value the open sharing of resources and encourage high quality peer reviews. Further transparency such as this  is necessary for a thorough verification from other institutions.

The current systems that facilitate research can often affect our interpretations of the subjects being presented, whether intentionally or not. It is important that we acknowledge that these biases may be present so that they can be scrutinized by various other external researchers. In order for scientific research to become trustworthy, we must create a system that rewards the peer review process, rather than simply serving to further a scientific career. Otherwise, the time and effort of researchers is wasted on scientific reports that are misinterpretations or misrepresentations of the real truth.

 

Work Cited

Boutron, Isabelle, and Philippe Ravaud. “Misrepresentation and distortion of research in biomedical literature.” Proceedings of the National Academy of Sciences of the United States of America vol. 115,11 (2018): 2613-2619. doi:10.1073/pnas.1710755115

Ioannidis JPA (2014) How to Make More Published Research True. PLoS Med 11(10): e1001747. https://doi.org/10.1371/journal.pmed.1001747

Ioannidis, John P A et al. “How to survive the medical misinformation mess.” European journal of clinical investigation vol. 47,11 (2017): 795-802. doi:10.1111/eci.12834