Does the Stanford Covid Study’s Debunking Harm “Science” Itself?
Take that now discredited Stanford University Santa Clara county study that was spun throughout the world as evidence that Covid’s only as deadly as flu (it’s not – in early May 2020 in the U.K. the case fatality rate of Covid was 29%!)
Its coauthor Stanford celebrity academic Ioannidis even found fame for exposing other scientists’ mistakes in the so-called Replication Crisis:
Now, faulty statistical calculations, media tour claims by Ioannidis that the study faulty as it is doesn’t even back-up and a cache of emails exposing apparent mismanagement of the study and its publication put Ioannidis (and co-author Bhattacharya) in the dock.
Turns out that for Ioannidis common sense dictated Covid was only as deadly as flu, everyone was being hysterical and “hey presto!” he found a scientific way of “proving” this prior common sense assumption:
Only the methodologically faulty and statistically dodgy study doesn’t even “prove” this canard.
Ioannidis appears to have misrepresented the anyway faulty study’s implications in interviews on Fox etc where he promoted the idea Covid is “just like flu”.
How can parents trying to decide if they should send their children back to school, who don’t have the time to read everything out there so that they can discern the truth in the misinformation spinning around, know what is common sense?
The statistical methods Ioannidis used in his study, as Columbia University’s Andrew Gelman shows, discredit its findings anyway, even if the study hadn’t been paid for partly by an airline owner:
The irony that Ioannidis is the founder of the replication crisis movement that seeks to discredit not only scientists but the entire scientific method itself would be delicious if it didn’t have such a real world impact when it informs government ministers’ self-serving “anti-expert” rhetoric.
Ioannidis’s and Bhattacharya’s Stanford Santa Clara study was taken up globally by those who wish to minimise the impact of Covid and justify dangerous early re-openings of our economies.
Now it is discredited on the grounds of circular reasoning: the modellers thought Covid killed as few as seasonal flu, & this determined their results.
As I write about in my Two Tribes piece
whether or not you value the “Infected Fatality Rate” over the Case Fatality Rate (CFR) as your favoured metric is a philosophical and increasingly an ideological choice.
So even if the Santa Clara study’s implication that it’s a low Infection Fatality Rate statistically arrived at which justifies government premature economy reopenings that endanger lives, why would you value such an implication over, say, the real world visible data of graves being dug in New York City’s parks?
Talk about burying your head in the sand!
Or being baffled by BS!
The real world data should be enough to show that any academic data that suggests Covid is normal is itself faulty.
Data are not just somethings you see on computer screens!
There’s no common sense objective reason why one would chose at this stage in a pandemic either Infection Fatality Rate or Case Fatality Rate over the other as the metric: unless you’re promoting the idea that everyone needs to open the economy up without a proper test, track, system in place.
Then you might be tempted to peddle a metric that undervalued the danger we face from this terrible disease.
Indeed, even apart from the real world data that show us the impact of the disease is actually that its virality, infectiousness, & survivor discomfort which say: avoid this disease, at all costs:
see for example -
By the way, I’ve been mulling a new metric to compare how we’re doing:
Let’s chart the deviation/volatility between IFR and CFR.
UK’s Case Fatality Rate last Wednesday was 29%:
In countries like HK, NZ, Aus, Taiwan, China etc that are testing massively, the CFR and the IFR converge.
Anyway, back to Ioannidis and his campaign against the scientific method itself (hypothesise, test, verify, report honestly, be open to correction: rinse and repeat).
As far as I can see, some of the leaders in the replication crisis movement Ioannides helped found are only interested in one province of what used to be known as the natural sciences:
The province of mathematics’ related disciplines.
Indeed, it appears that many of them are mainly interested only in a sub-province of this province: data analytics or (old fashioned name) statistics.
I recognise that data analytics is a pretty important area – it inspires the computer algorithms which now underpin our civilisation and how I am able to write this text.
Yet, it’s only the Scilly Isles, if the rest of science is the earth. “How birds fly?” “What’s love?” “What happens as one dies?” “How can form be empty and emptiness form?” are examples in the multiverse sized space of the Natural Sciences that lies outside their parochial standpoints. “There are more things in heaven and on earth than are thought of in your philosophy” and all that could have been written for these people alone.
They’re primarily focussed on what the Dalai Lama refers to (in this teaching on science https://m.youtube.com/watch?v=uQDmEC_qmc4) as “manifest things” and on a certain subset of the “hidden things” which they use inference to see.
Their main instrument of seeing these “hidden things” is the product of the Scilly Isles – data analytics. This parochial view of science blinds them to all the other ways of seeing, alas, it would seem. Invisible to them are the graves being dug in New York City parks!
Many of the rest of us infer things too from the “hidden world” without solely or biasedly privileging data analytics.
Harvard Business School’s Shoshana Zuboff nails this point perfectly: these Instrumentarians, as she calls them, are not interested in the life of the mind or in what is aggregated into the mind’s material and spiritual attributes. Consciousness is only of interest to them in terms of its impact on behaviour.
Along with the Behaviourists (whose theories about the mind are grounded in experiments on pigeons at Harvard), it doesn’t matter what we think. All that matters is how we behave and how knowing that behaviour allows us to be “herded, tuned and conditioned” into servicing whichever ideological framework those with power have decided a generation’s people must serve.
Quantum physics for this instrumentarian tribe is just a way of epating the bourgeoisie. It’s just another way of undermining Newtownian mechanistic laws of universe, not in the service of advancing scientific truth, but in the service of serving today’s economically expedient prejudices.
It suits behaviourists and their followers to behave as if individual humans are just objects (pigeons) that respond in certain path-dependent ways to stimulae, like Skinner’s experiments on pigeons hopping around hot plates in his Harvard kitchen.
Faculty, Deep Mind, Palantir, etc are perfect instruments for this work as some of those driving their tech believe wholeheartedly in this Instrumentarian logic as if it was a religion. They don’t feel that they are part of the herd that’s being herded, conditioned and tuned. But the rest of us are.
Today we have Internet wrought tools that Skinner with his pigeons, hot plates and a basement in Cambridge could not even have imagined existing with which to “herd, tune and condition” in accordance with their ideas of the good.
That’s the Scilly Isles sized province of Science that Ioannides and the replication crisis they did so much to feed service. How ironic that in their unconcern for truth the doubts they sowed finally caught up with them and exposed their own mistakes!
Talk about a revolution eating its young.
Discussing Zuboff’s “The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power” before Covid struck none of this seemed that urgent really.
Now many of us are facing the existential question of “do I trust the govt to look after my children’s welfare if I send them to school on June 1st, or am I part of their herd?”
Science provides and provided myriad ways of halting this virus (Hong K, NZ, SK, Taiwan,…) and I have catalogued here how many of those who held up the replication crisis as a reason to doubt all science that they found to be inconvenient responded to the science that said “we can suppress this virus”: “The story of the UK’s ‘100k deaths is okay’ strategy”
The U.K. Sunday Times summed up the British prime minister’s senior adviser’s approach to Covid rather well:
That people who subscribe to these views influence policy that impacts on millions if not billions of lives should give us a renewed sense of urgency. What Zuboff reveals to us of the thinking of such instrumentarian logic is not now, as it was perhaps in the summer of 2019, of some minor academic importance.
The models which correctly predicted what % of the Spanish population has been infected (models confirmed by empirical research testing the blood of 60k people), predict 3.8% of U.K. population are infected with Covid.
So we need 750k deaths to get to 57% “alleged immunity” which without a vaccine doesn’t even seem possible, according to scientists. That’s what the science is saying. Why aren’t those who promoted the replication crisis not now listening to this?
All that appears to have changed is that the policy of herd immunity professed publicly by those close to the government has gone underground.
It’s not the science or the scientists we must be doubtful of, it’s the ideology of instrumentarianism that underlies much of it that we should be hunting out and exposing to sunlight as Gelman at Columbia and Buzzfeed have done to the Stanford Santa Clara study.
There’s no crisis in science itself. We are all scientists these days. We make choices based on data we perceive in the real world. Only in one sub-province of a province of science is there a problem. One of Ioannides’s findings as part of his replication movement is that the crisis was not really caused by scientists’ dishonesty. It was just that their data don’t back-up their claims. They were operating in good faith.
Now we all have an opportunity to judge whether or not Ioannides himself was operating in good faith.
So this is not a crisis for science any more than Ioannides’s replication crisis was.
It is however a wake-up call for the rest of us: if the science contradicts common sense, don’t necessarily trust that the scientists are right and we are wrong. After all there’s nothing normal about the numbers of excess deaths afflicting our civilisation, as this Oxford paper so lucidly explains:
Measuring excess mortality: the case of England during the Covid-19 Pandemic
Indeed reading DeFoe’s seventeenth century Diary of the Plague Year recently, I was struck by how, despite all our innovation in statistics and government transparency, it was the excess deaths published in the weekly Bills of Mortality which were used to measure the severity of that seventeenth century plague:
So Science is not threatened, neither by Ioannides’s mistakes, nor by his replication crisis insights.
Science is only threatened when we close our eyes to the data we see around us.
We can’t outsource truth and decisions about what is true and what is not true, even to scientists as famed as Ioannides.
We must continue to test our common sense interpretation of real world data (they’re digging graves in New York City’s parks!) against what scientists find in their statistical models.
And if there’s a contradiction between the two data sources?
Don’t assume that what you see with your eyes and what you feel is wrong, just because a data scientist says it is and has a devilishly complicated way of “proving” what contradicts common sense might be true.
When he was accused of bribery the founder of the scientific method Francis Bacon defended himself by saying: I may have taken money, but I didn’t let it affect my judgment.
It’s deeply ironic that the man who has done so much to undermine faith in the scientific method himself in our age should use, centuries later, almost the same defence.
