White hat bias
White hat bias (WHB) is a purported "bias leading to the distortion of information in the service of what may be perceived to be righteous ends", which consist of both cherry picking the evidence and publication bias.[1] Public health researchers David Allison and Mark Cope first discussed this bias in a 2010 paper and explained the motivation behind it in terms of "righteous zeal, indignation toward certain aspects of industry", and other factors.[1]
The term white hat refers idiomatically to an ethically good person, in this case one who has a righteous goal.
Overview
This initial paper contrasted the treatment of research on the effects of nutritively-sweetened beverages and breastfeeding on obesity. They contrasted evidence which implicated these behaviors as risk and protective factors (respectively), comparing the treatment given to evidence for each conclusion. Their analyses confirmed that papers reporting null effects of soft drinks or breast-feeding on obesity were cited significantly less often than expected, and, when cited, were interpreted in ways that mislead readers about the underlying finding. Positive papers were cited more frequently than expected. For instance, of 207 citations of two papers finding no effects of sugared soft drink consumption on obesity, the majority of citations (84% and 66%) were misleadingly positive.[1]
A meta-analysis had been reported showing that industry-funded studies reported smaller effects than did non-industry-funded studies,[2] the implication being that industry funding leads researchers to bias their results in favor of the funder's presumed commercial interest. Allison and Cope's reanalysis of these data indicated that it was poor studies that found larger effects, and that the industry-funded studies were larger and better run: a finding consistent with a white hat bias, and suggesting that the true effect of sugar-sweetened beverages is smaller than most studies report.[citation needed]
Allison and Cope suggest that science might be protected better from these effects by authors and journals practicing higher standards of probity and humility in citing the literature. Young, Ioannidis and Al-Ubaydli (2008)[3] discuss related concepts, framing scientific information and journals in the context of an economic good, with the goal being to transfer knowledge from scientists to its consumers, suggesting that acknowledging the full spectrum of effects on publication and treating addressing the effects as a moral imperative may aid this goal.[citation needed]
Controversy
Having shown that industry studies were well run but that publication and citation bias existed against negative findings, and as predicted from a WHB effect, Allison—being funded himself by the food and beverage industry[1]—became the subject of a media report by ABC condemning the influence of industry on diet science.[4]
See also
- Academic bias
- Replication crisis
- Cherry picking
- Funding bias
- Publication bias
- Woozle effect
References
- ^ a b c d Cope, M B; Allison, D B (2009). "White hat bias: Examples of its presence in obesity research and a call for renewed commitment to faithfulness in research reporting". International Journal of Obesity. 34 (1): 84–8, discussion 83. doi:10.1038/ijo.2009.239. PMC 2815336. PMID 19949416.
Editor's note: Atkinson, R L; MacDonald, I (2010). "White hat bias: The need for authors to have the spin stop with them". International Journal of Obesity. 34 (1): 83. doi:10.1038/ijo.2009.269. PMID 20062107. - ^ Vartanian, Lenny R.; Schwartz, Marlene B.; Brownell, Kelly D. (2007). "Effects of Soft Drink Consumption on Nutrition and Health: A Systematic Review and Meta-Analysis". American Journal of Public Health. 97 (4): 667–675. doi:10.2105/AJPH.2005.083782. PMC 1829363. PMID 17329656.
- ^ Young, Neal S; Ioannidis, John P. A; Al-Ubaydli, Omar (2008). "Why Current Publication Practices May Distort Science". PLOS Medicine. 5 (10): e201. doi:10.1371/journal.pmed.0050201. PMC 2561077. PMID 18844432.
- ^ "Is 'Big Food's' Big Money Influencing the Science of Nutrition?" (2011) ABC video.
Further reading
- Kaiser, K A; Cofield, S S; Fontaine, K R; Glasser, S P; Thabane, L; Chu, R; Ambrale, S; Dwary, A D; Kumar, A; Nayyar, G; Affuso, O; Beasley, M; Allison, D B (2012). "Is funding source related to study reporting quality in obesity or nutrition randomized control trials in top-tier medical journals?". International Journal of Obesity. 36 (7): 977–981. doi:10.1038/ijo.2011.207. PMC 3288675. PMID 22064159.
- Bes-Rastrollo, Maira; Schulze, Matthias B.; Ruiz-Canela, Miguel; Martinez-Gonzalez, Miguel A. (2013). "Financial Conflicts of Interest and Reporting Bias Regarding the Association between Sugar-Sweetened Beverages and Weight Gain: A Systematic Review of Systematic Reviews". PLOS Medicine. 10 (12): e1001578. doi:10.1371/journal.pmed.1001578. PMC 3876974. PMID 24391479.
- v
- t
- e
- Acquiescence
- Ambiguity
- Affinity
- Anchoring
- Attentional
- Attribution
- Authority
- Automation
- Availability
- Belief
- Blind spot
- Choice-supportive
- Commitment
- Confirmation
- Compassion fade
- Congruence
- Cultural
- Distinction
- Dunning–Kruger
- Egocentric
- Emotional
- Extrinsic incentives
- Fading affect
- Framing
- Frequency
- Frog pond effect
- Halo effect
- Hindsight
- Horn effect
- Hostile attribution
- Impact
- Implicit
- In-group
- Illusion of transparency
- Mean world syndrome
- Mere-exposure effect
- Narrative
- Negativity
- Normalcy
- Omission
- Optimism
- Out-group homogeneity
- Outcome
- Overton window
- Precision
- Present
- Pro-innovation
- Proximity
- Response
- Restraint
- Self-serving
- Social comparison
- Social influence bias
- Spotlight
- Status quo
- Substitution
- Time-saving
- Trait ascription
- Turkey illusion
- von Restorff effect
- Zero-risk
- In animals
- Cognitive bias mitigation
- Debiasing
- Heuristics in judgment and decision-making