Dr. Terence Kealey and the Myth of Scientific Objectivity

ByCrossFitAugust 11, 2019

“I think there is a vast myth that scientists are somehow objective and honest,” Dr. Terence Kealey said during a presentation at a CrossFit Health event at CrossFit Headquarters on March 9, 2019.

Kealey is a former vice-chancellor of the University of Buckingham, a professor of clinical biochemistry, a scholar affiliated with the Cato Institute, and author of the book Breakfast Is a Dangerous Meal. During his presentation, he discussed the myth of scientific objectivity, drawing examples widely from history as well as his personal experiences within many of the most reputable scientific institutions.

People will “very quickly rationalize and create a little paradigm to justify what they believe is true,” Kealey explained, emphasizing with a bit of wry humor that scientists are, in fact, people and engage in the kind of data-distorting practices he then went on to describe. “We commit to paradigms, and then we bend the data to it.”

The problem of science’s commitment to paradigms, Kealey claimed, is exacerbated by several factors, including government involvement and chummy funding practices whereby support is given only to those intellectual allies with whom one agrees.

“Now, you get money not for being right; you get money for satisfying the prejudices of the people who sit on the committees in the NIH and NSF,” Kealey said.

To illustrate the problem of government involvement, Kealey detailed the forces that lent longevity to Ancel Keys’ false diet-heart and lipid hypotheses. Generations of doctors were taught the faulty lipid hypothesis “because Ancel Keys’ rebuttal of his own data was not accepted by the federal government,” Kealey claimed.

Regarding scientists’ tendency to want to give money to others who confirm their beliefs, Kealey said simply, “This is swagger science.”

To read a full transcription of the presentation, click here.

Comments on Dr. Terence Kealey and the Myth of Scientific Objectivity


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Doug Tran
August 12th, 2019 at 12:54 am
Commented on: Dr. Terence Kealey and the Myth of Scientific Objectivity

Crossfit.com please please please stop posting these articles that are clearly biased. Kealey is a professor in the UK and where I can not comment on what happens on the other side of the Atlantic I truly believe in our scientists, professionals and people in position for expert opinions. Can we hold this same article to hold benefits of Crossfit as unbiased and pure?

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Russ Greene
August 12th, 2019 at 3:15 pm


This is a video, not an article. Kealey's nation of origin is irrelevant to his assessment of the practice of science, which is transnational by nature. An a priori belief in scientists and "expert opinions" is neither consistent with the principles of science, nor with available evidence. Moreover it is not even internally consistent - the experts in whom you trust themselves disagree with each other.

Generally, thinking that any one researcher is correct means thinking that others, their peers, are wrong. Take the replication crisis. Do you believe in the original researchers whose work later failed to replicate, or in the researchers whose experiments replicated their methods, but not their results? Or take another example: if you believe in the work of Professor John Ioannidis, then you must not believe that most research findings are accurate.

Perhaps it is time for you to stop chasing belief in science and instead pursue critical thought. You will not by such means find truth, but you may evaluate the extent to which diverging models approximate it.

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Richard Sheehan
August 14th, 2019 at 11:15 pm

Couple of points here. First, I think it's most useful to approach the question of scientific objectivity from a Bayesian (statistical) perspective. We all approach any problem with some guess as to the best solution. Think of those as our paradigm or prior beliefs - or Bayesian prior probabilities. Those prior beliefs could be incredibly incorrect, and Dr. Kealey certainly points out some important examples. But the role of science and scientists is to seek evidence to confirm or to refute or to extend that paradigm. Copernicus, Darwin and Einstein, for example, all took a very different approach to what had been conventional wisdom after extensive analysis of data that was, at times inconsistent with that conventional wisdom. And you could likely have made the argument that 97% of astronomers or biologists or physicists initially disagreed with their findings. That percentage changed dramatically albeit sometimes slowly as scientists revised their Bayesian priors. But when you've operated for a very long time under a certain set of beliefs, to think that those priors are going to change quickly is simply unrealistic in most cases.

Second, most of us have neither the time nor the expertise to wade through conflicting perspectives on statins, cold fusion or the impact of a change in a Federal Reserve policy. The implication is that our priors will be formed, for better or worse with a very limited information set heavily dominated by experts. This isn't necessarily a problem, and from a Bayesian perspective, the phrase would be - or should be - that our prior beliefs/probabilities are flat or disperse. With statins as an example, conflicting evidence suggests that maybe whether statins will or won't help might be equivalent to a coin flip. We simply cannot get a definitive answer, and maybe the best thing we can say with that evidence is that rather than there's a 97% probablilty that a statin might help and won't hurt is that there's a 60% or 40% or ... probability that it might help - or that the benefits outweigh the costs.

Third, and directly on the title, The Myth of Scientific Objectivity, virtually all the scientists that I know, including some with whom I strongly disagree on some points, want to get things right. Can they/we make mistakes? Absolutely! But when limitations of our analysis are pointed out, often in the review process, we go back and re-experiment or re-analyze and attempt to rectify those errors. And Keyes is a good example of doing just that. I think that problems are more likely to arise when you have business funding with an aim at marketing. And that would hold whether we're talking statins or breakfast cereal or cigarettes. But it doesn't hold in so many other areas, whether you're talking Boeing hiring researchers to determine optimal wing shape for lift and drag or a financial firm hiring researchers to determine whether it's cost-effective to move their transmission station 100 meters closer to the trading floor.

That scientists can be wrong isn't a news flash. That they are not scientifically objective is, I believe, fundamentally incorrect in general although there certainly are exceptions. And it's important to point out those exceptions. But we shouldn't be talking about belief in science vs. critical thought as though they're two different entities.

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