Disclosing bias may not be worth the risk

When conducting experiments to expand the knowledge within the sciences, it is important to remove as many biases as possible. Biases can mislead the researchers and can also be a major factor to the irreproducibility of the experiment. But, one can argue that it is impossible to remove all biases. Could this mean that all research is flawed? Jared Horvath pointed out that, “In actuality, unreliable research and irreproducible data have been the status quo since the inception of modern science.” This is an alarming but true statement and can sway people’s faith in the scientific and pharmacological community. After the recent vaccine scare, which was considered as unwarranted, it appears that people have lost trust in the large drug producers. The main drive of the “anti-vaccers” is the possibility that these companies have a financial bias. What would people do if they knew of the many other biases that exist when vaccines (or other drugs) are developed and evaluated? Jared Horvath says that biases should be communicated honestly to the public but I think it will make the situation even worse. Brian Shilhavy points out an interesting conflict of interest of the CDC. Because the CDC has a budget to purchase 4 billion dollars worth of vaccines from companies, he questions whether they should be allowed to oversee the safety of vaccines. Regardless if this claim holds water, it still puts distrust between the public and drug companies. Bias does not only impact the irreproducibility of an experiment but also the faith of the public.

I believe that it would be irresponsible and unscientific for someone to follow a claim without doing proper research. However that philosophy should also apply to people willing to take vaccines. Why should they simply believe that vaccines are safe? The truth is, they shouldn’t. But, how can any of these parties conduct literary research without access to the proper resources. It is up to scientists and physicians to provide access to easy-to-understand [raw] data to educate the public. This is a better solution in my opinion than disclosing all “possible” biases.