Biases in Science

in #science8 years ago (edited)

From small to large studies, early or highly sited studies, scientists aren't immune to cognitive biases in their work.


Credit: Petr Kratochvil/Public Domain

Researchers have been studying how science is done by asking how common biases are, how they affect different disciplines, what are the factors that cause these biases, and how to reduce bias in scientific work. The research paper was published in Proceedings of the National Academy of Sciences. Over 3,000 meta analyses with 50,000 research studies were used to map the biases in science.

These are the types of biases that were found:

  • Small-study effect: when studies with small sample sizes report large effect sizes.
  • Gray literature bias: the tendency of smaller or statistically insignificant effects to be reported in PhD theses, conference proceedings or personal communications rather than in peer-reviewed literature.
  • Early-extremes effect: when extreme or controversial findings are published early just because they are astonishing.
  • Decline effect: when reports of extreme effects are followed by subsequent reports of reduced effects.
  • Citation bias: the larger the effect size, the more likely the study will be cited.
  • United States-effect: when U.S. researchers overestimate effect sizes.
  • Industry bias: when industry sponsorship and affiliation affect the direction and size of reported effects.

Researchers looked into these factors that may increase the risk of bias:

  • early career status
  • isolation of working alone without colleagues
  • size of collaboration between scientists
  • involvement in misconduct
  • gender of researchers- pressure to publish

The most biases come from small studies. Studies that made it into peer-reviewed journals or became highly cited also were more likely to overestimate the effects of their research. When a small sample size of a population is taken, the results gained may not actually represent the reality overall, which is why it's an overestimation effect. Early studies without verification, especially U.S. studies, tended to report more extreme effects about the matter they were studying. The frequency of publishing, the gender of the researcher, and the incentive to publish the not have more bias than other studies.

The influence of different biases changes in different disciplines, yet seems to be a constant with respect to individual scientists. Senior author John Ioannidis said:

"This is particularly driven by the social sciences, so if you broke scientific fields into big bins of biology, medicine, physical sciences and social sciences, it seems that the social sciences are seeing the more prominent worsening of these biases over time."

Bias in behind some areas, but nonexistent and many others. Therefore, bias does not undermine all of the scientific work. A one-size-fits-all solution is not likely, as interventions to resolve biases is likely required to be tailored to the specific problems found in individual disciplines or fields of scientific research.

Making science better can happen on its own, as was done with physics where they decided to replace tiny studies done by small teams with multi-team collaborative models. Each field of science is different with different bias profiles, and it's probably best for each to choose the best way to reduce biases affecting their particular field. Ioannidis said "this has to be a grass-roots movement". Scientists need to understand and want to do this for the good of their science. Institutions and funding agencies should not enforce these changes.


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2017-03-27, 10:10am

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Hmm, I wonder if this bias extends to the studies that showed that scientists can be bias in their studies...

Don't know if there's a formal name for it, but a graduate student explained it to me, back when I was in college... it's the particular confirmation bias in which experiment/study observation methods have greater acuity in detecting the factors that prove a theory than those which disprove a theory

Yeah, studies that confirm something, especially those that overestimate the effect as larger, get published more because they are making a claim for something hehe. I made a post a few months ago on a similar issue, that more negative research is needed to validate the positive claims in studies.

Another cognitive bias that could be worth considering is confirmation bias through which people search for information that confirms a preconceived theory and ignore information that rejects that theory.

Indeed, thanks for the feedback. These biases are for scientific research only, as you can see by what they are.

Thank you! Good to know it does apply and the specific term for it!

Good post, @krnel.
I know bias and fraud are two different things but I thought you wouldn't mind if I suggest a book I like a lot:
"The Great Betrayal: Fraud in Science" by Horace Freeland Judson

Thanks for the recommendation.

There is the grant bias too, I need to pay my mortgage...lolol great article.

Another part of the grant bias is that it shapes the entire research. If big pharma wants X to be proven, then all the front companies offer grant streamlining in those areas. Of course, none of this is published, its all rumors.

And we all know that the person who frames the question is the one with the biggest control on any answers.

I worked for a Professor with Ph.D In family health and dynamics, she was so dysfunctional she needed a handler outside of the classroom. She was a good hearted person though, the system crushed her.

She told me the whole dirty story regarding government and transnational corporate science grants. Around that time I decided not to go into nursing, I like ethics and lab studies. Plus this was my first red pill experience outside of my own personal experience trying to heal my so called fibromyalgia diagnosis.

thanks for giving this important message..
Resteem post.!! @krnel

Greetings, @krnel! Interesting subject, learning a lot.

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