Gary Smith is coming out with a new book, “Distrust: Big Data, Data Torturing, and the Assault on Science.” He has a lot of examples of overblown claims in science—some of these have appeared on this blog, and Smith takes pretty much the same position that I take on these things, so I won’t talk about that part further.
Rather, I want to talk about the big picture that Smith paints, which is the idea that science is very important to our lives, and bad science degrades that trust. Ironically, I think this sort of attitude is also behind some of the anti-reform movement by leading academics: they think science is just wonderful and they’d prefer if people just kept quiet about scientific errors or chalked things up to “the self-correcting nature of science.” Smith and I are more in the clear-the-rotten-apples-out-of-the-barrel camp.
Smith has a broader perspective than just talking about junk science. He also talks about misplaced technology enthusiasm, from bitcoin-as-savior to chatbots-as-AI. One thing he doesn’t really get into is the political angle, the idea that bad actors are sowing distrust as a tactic to reduce respect for serious science. Familiar examples are industry-funded junk science on cigarettes and climate change, which then gets picked up by ideologues. And then there are the HIV/AIDS denialists, which seems more like contrarianism, and the covid denialists, which has a political angle. I’m not saying that Smith should’ve discussed all that—there’s a limit to how much can fit in one book—; I’m just bringing it up to emphasize that distrust in science is not just an unfortunate byproduct of frauds like the disgraced primatologist and confused people like the ESP guy, it’s also something that a lot of people are fostering on purpose.
When we write about himmicanes and forking paths and multilevel modeling and all the rest, that’s just a small part of the story. And one of the challenges is that we can’t simply root for “science” as an institution. We root for the good stuff but not the bad stuff.
We live in a world where the head of the CDC claimed no one ever mentioned viruses mutating to escape immunity or waning immunity to her:
https://m.youtube.com/watch?v=I_hYgIpxM4A
This is literally a yearly experience for people that an expert of infectious disease claims to be unaware of. There are zero examples of respiratory viruses that yield lasting immunity, and zero examples of IM vaccinations that induced the mucosal immunity required to prevent infection/transmission.
And of course if you create a monoculture of people with immunity towards the exact same spike protein it will simply mutate around it. That is why your body raises immunity to multiple proteins at once after seeing the common natural variations.
How is ignoring key virology 101 facts “science”? It just isn’t. And neither is claiming “95% effective” by taking a snapshot of two exponential growth curves. Looks like the same method was used to claim angiostatin was going to cure cancer in the 1990s, but nobody learned from it. Learning from errors is a key part of science.
Please just call it “research” instead and leave “science” to methods where people regularly reproduce each others results and can make meaningful predictions about the future.
Anoneuoid –
> We live in a world where the head of the CDC claimed no one ever mentioned viruses mutating to escape immunity or waning immunity to her:
You keep referencing that.
Consider two possibilities. One is that she meant something like: “I never heard anyone mentioning waning and I didn’t realize it would likely happen.”
Or something like: “You asked me about mistakes made and I think at times we were painting too optimistic a picture, for example when we spoke about the effectiveness of the vaccines, initially we didn’t talk enough about the likelihood of waning.”
Which do you think was the most likely intended waning?
Intended meaning, that is.
You’re very badly misinterpreting that quote. Walensky was criticizing the CDC’s excessive optimism in its messaging, and the failure to communicate appropriate levels of caution about the likelihood immunity wouldn’t be permanent.
I was giving the benefit of the doubt by assuming incompetence. Holding back key information, or purposefully avoiding collecting it to begin with (a form of lying that is common in medical research) would be worse.
Anyway, no wonder people distrust this “science”, it is an imposter.
Lol.
So creating a misleading interpretation, rather than noting she was acknowledging an error in approach to messaging, is giving her the benefit of the doubt?
Pathetic. You misrepresented her.
We could give you the benefit of the doubt and assume it was unintentional – but your resistance to acknowledging error – error likely explainable by the biasing influence of your viewpoint agenda – only makes that more difficult.
Dan –
> You’re very badly misinterpreting that quote.
Misinterpreting or misrepresenting? I’d like to think the former but the lack of accountability beings that into question.
It isn’t exactly science denial, but it seems to me that election denial is pretty much the same thing, and even more important.
Election denial does get into some serious statistics denial issues, especially wrt to things like risk-limiting audits.
Looking forward to reading this book!
I think the primary problem is the sheer volume of low-quality published papers. We need a way to weed out the junk and to signal to the public which findings are robust. I’ve proposed replacing peer review with peer replication: https://blog.everydayscientist.com/replace-peer-review-with-peer-replication/
At minimum, I think editors and reviewers should demand that authors do their data acquisition and analysis blinded and stop publishing ridiculously small p-values: https://blog.everydayscientist.com/unbelievably-small-p-values/
Yep, except rather than “replace” it is a return to using methods that work. Also return to testing the research hypothesis rather than a default null hypothesis.
It would be very simple, but unfortunately requires reassessing and possibly giving up some sacred cows. It likely will not happen outside the context of something akin to the reformation, resulting seperation of science and state. I hope it happens though.
Not all science is created equal. Confidence in old school “hard” science such as nuclear physics or the Krebs cycle is as high as ever. Everyone “trusts the science” of the microchips in their smartphones.
Doubts emerge when it comes to predicting complex systems such as, for example, economies, politics, global environment, pandemics, and so on.
There’s good reason to doubt that later. They have inherent disadvantages (difficulty narrowing down and determining required inputs, lack of true experiments, computational complexity,…) and their track record isn’t great.
Perhaps it was a mistake for these attempts at predicting complex systems to appropriate the cache of the hard sciences?
+1
jbayes wrote: “There’s good reason to doubt that later. They have inherent disadvantages …”
True. But it seems that the doubters in those cases* pretty much all fall in a particular political camp. Folks who don’t like what the solid parts of the science in those areas are clearly telling us, and claim that the edge cases disprove the solid stuff.
*: Economics and poliitics are, of course, inherently political. But we know a lot about global warming (if we don’t stop flying/driving so much, it’s going to be a disaster) and pandemics (masks and vaccines work); these things shouldn’t have been up for discussion.
This is a good point. Medicine as a subset of biology is often very hard to analyze. As an example an apparently simple question such as health effects of coffee drinking has had a variety of answers over the years. What is the best diet, the best sleep pattern, and the best blood pressure target? Each of these questions and many others has many advocates but clearcut straight answers are more elusive. Advocates are full of certainty but not data. And we have more advocates than thinkers.
There may be some sort of equilibrium, or possibly cycle, for the fraction of scientific news stories and reports that are actually science and the fraction of scientific news stories that are primarily written to push some line or other. Credibility gained attracts “science shows that…” which eventually reduces credibility, which makes this a less effective debating tactic, which reduces its popularity…
> Confidence in old school “hard” science such as nuclear physics or the Krebs cycle is as high as ever. Everyone “trusts the science” of the microchips in their smartphones.
Is it? I’d like to believe this point, but things like 5G conspiracies, new-age “quantum mysticism,” denialism of the germ theory of disease, etc. make me a bit more pessimistic.