Bias: an unavoidable word related to Science and Research

Research bias, defined as the process where the scientific researcher influences results for a specific outcome, is a loaded phrase that has become an important topic to address in scientific studies. Unfortunately, the cyclic process necessary to become a successful, funded scientist leaves many opportunities for the scientist to introduce bias into their research. This includes, but is not limited to, research design, hypothesis development, previous research results, choice of analysis, and getting a grant to keep a job.

A relatively new field in science, mainly because of advanced technology and resources, is big data research. Big data research, regardless of subject, relies on the scientist either to process data with previously generated algorithms/codes or to create their own processing method. When picking or creating the “correct” algorithm or code to use, it is easy to unconsciously choose a method that best agrees with one’s proposed hypothesis or desired results. For example, it is hard to decipher whether the data has no effect because there was indeed no effect or if the wrong algorithm/methods of analysis were used.

There needs to be accountability. One way to do this, specifically with big data research, is by sharing raw data, coding algorithms, and specific data analyses methods through open source websites such as GitHub: Science. Not only will this encourage other scientists to reproduce results, it will also create an environment that encourages collaboration. If raw data and codes are made available through open source, anyone can provide feedback or ways to better the analysis process. Another important aspect is allowing people to upload or provide data on negative results. In most cases, negative results are not publishable. This would give a research group an opportunity to get feedback from other people in the community on their negative results and perhaps even help others that are pursuing the same research question. 

Now, more than ever, it is necessary to expect experimenters to produce unbiased and reproducible results. We have the resources to do this. It is up to the science community to create new standards that allow for accountability and reproducible results while encouraging collaboration.