Friday, April 28, 2017

Avoiding Cognitive Bias in Science

Cognitive bias is a part of our everyday lives.  Social influences, emotions, data processing errors, flawed memories, and the brain’s inability to fully process information all contribute to cognitive bias.  From the celebrity bandwagon effect to the court room, we all experience cognitive bias most of the time without even realizing it. 

There is no question cognitive bias has been a long-standing issue in scientific research.  The pressure to publish in high impact journals with a quick turnaround between publications is higher than ever.  Techniques and methods are becoming so complicated, that researchers often don’t know how to correctly analyze the data or lose sight of the principal question.  Nowadays, presentations of data are completed in a timely manner.  Presenters provide a large amount of data trying to convince audience members of their conclusions.  These presentations happen so quickly, that audience members cannot fully grasp everything that is being thrown at them.  In addition, when statistics are reported and readers see a p value of 0.05 or less they assume statistical significance without ever seeing the raw data or full statistical analysis.  Chances are a lot of data presented are flawed in their statistical tests.  If the raw data is presented it would allow others to conduct their own statistical analysis, which would more than likely result in different findings.

To fix the issues associated with cognitive bias, the field must first recognize that there is an issue.  By becoming more transparent with methods, techniques, raw data, analyses and conclusions in an open science environment, some of these issues will be avoided.  By pre-planning and pre-reporting methods and analysis, a researcher will be tied to their analysis which will eliminate some of the initial cognitive bias that is seen when scientists analyze collected data.  Rather than finding a test to fit the collected data, the data would be analyzed with a pre-chosen test.  In addition, blind data analysis should be used to limit bias.  Rather than collecting enough data to get the expected results, analysis should be conducted blind and confirmed by multiple personnel to further avoid cognitive bias.  Although these are simple steps that can be pursued to avoid cognitive bias in the field, they are not the only solutions, nor are they the correct answers.  They are simple ways that can help avoid biases in science, something that all researchers need to keep in mind when conducting their work.

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