WHITE HAT BIAS:
The distortion of research-based information or evidence in a way to promote what it is believed to be righteous ends (e.g. righteous policies, rules, actions, or changes to the status quo)1. In other words, using emotionally-charged rhetoric when interpreting scientific data, or using emotionally-charged rhetoric in scientific studies to promote ‘good’ or ‘ideal’ beliefs or actions.
Science has always intended to be objective; such that research questions are established in advance of a study, a protocol for the study is developed, data is collected without interference of investigators or those being studied (i.e. subjects), results are observed and reported as is, the results are discussed in contrast to previous studies, and interpreted from multiple alternative perspectives (both favorable and unfavorable), and finally, a summary conclusion is drawn and stated.
White Hat Bias addresses the fact that, although scientists (those without conflicts of interest), strive to be beneficent, this effort towards goodness may interfere with their ability to be completely objective and disentangled from politics; and thus there is always a risk that emotion may interfere with how science is portrayed or communicated. Misleadingly citing research papers by using statements that do not accurately describe the results to convey a point, may lead to beliefs that inappropriately influence clinical practice, public policy, or future research1. Further, promoting righteous actions may alienate the public and lead to distrust of science, and exacerbate public health consequences.
White Hat Bias can be considered a type of Interpretive Bias, and can be avoided by interpreting and reporting scientific findings for what they are, as objectively as possible, and by avoiding emotional rhetoric. One should be aware that studies of interventions or other topics which are politically charged (e.g. prevention and treatment strategies for Covid-19)2, are highly susceptible to White Hat Bias. Also see: Ideological Bias, Academic Bias, Interpretive Bias, Publication Bias, and Biases of Rhetoric.
References:
1. Cope MB, Allison DB. White hat bias: examples of its presence in obesity research and a call for renewed commitment to faithfulness in research reporting. Int J Obes (Lond). 2010;34(1):84-8; discussion 83. (Link to Reference)
2. Bellos I. A metaresearch study revealed susceptibility of Covid-19 treatment research to white hat bias: first, do no harm. J Clin Epidemiol. 2021;136:55-63. (Link to Reference)