A lot's happened these past years relevant to how we think of the effects of "misinformation" and how to combat it. There was FB censoring lab-leak posts for a year before backtracking, there's Rogangate; there are also weird conspiracy theories about vaccines/5g/etc on SM.🧵
1/
The term misinformation is often abused, and deployed to belittle what is actually legitimate disagreement or uncertainty.
But claims that are scary and have no basis in truth do spread on SM and have large effects on important individual decisions.
The problem is real. 2/
Most of what people propose doing in response to this is more aggressive censoring. Of course this raises all sorts of questions about who would have the expertise to do this well, what it would actually accomplish in practice, what negative effects it would have, etc.
3/
Faced with the realization of these difficulties, I think some people, having reasonably concluded that censorship is not a great option, are left with the impression that there's not much a modern society could do to reduce the worst effects of the dissemination of confusion.
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But I think this misses something obvious, which is that misinformation thrives on a vacuum of trusted information.
4/
For all of our scientific institutions and public figures with scientific credentials, we have no well-known scientific figures who maintain impeccable and apolitical reputations by conservatively sharing only what scientific evidence shows at very high levels of confidence.
5/
Of course all sorts of decisions have to be made without the highest standards of evidence. But when we fail to distinguish what we truly know with high confidence from what is much more speculative but the basis of policy decisions anyways, we create fertile ground for mistrust
Every one of our nationally known public scientists has been willing to make extremely poorly supported statements that advance a policy goal of the politicians they work for.
It is no surprise they do not command broad trust across society.
The hybrid role of
"government scientist / political cheerleader"
is unlikely to go away, as it serves a political purpose. But one could imagine cultivating a second tier of truly trustworthy voices, who would never aim to advance policy and only be a refuge from confusion.
8/
These figures would not have told people in March 2020 that N95's don't work for regular people, only for Dr's, or in 2021 that we know that cloth masks are highly effective.
They wouldn't have said that vaccinated people can't transmit, or that vaccines prevent new variants.
9/
But they would be a trusted source about the safety and efficacy of vaccines and therapeutics, which is something the present system has left cynics without.
They could be a lifeline to people that are willing to trust someone they have never heard stretch the truth.
10/
tldr,
Rather than hoping to eliminate "misinformation" through extensive, highly competent censorship, we could aim to cultivate trustworthy information sources which have truly unimpeachable records of conservatively reporting evidence, never subservient to policy goals.
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The fast pace of recent decisions surrounding the use of boosters shots in low-risk populations comes amid a backdrop of significant expert dissent, and has been facilitated, in part, by bypassing scientific advisory committees.
It is surprising that against this backdrop, some universities (including, just today, @CarnegieMellon) have announced booster mandates for students, whose age make the risk/benefit calculation of boosters especially questionable, in part b/c of myocarditis events.
2/
I hope university administrators realize that with these policies, they are *requiring* students adopt a pharmaceutical intervention that domain experts do not even all agree should be *recommended* in this population.
Quick thread about re-randomization in randomized trials, illustrating the following two simple points:
*) re-randomization for balance is sometimes fine and doesn't affect significance thresholds
*) re-randomization for balance can sometimes cause big artifacts.
1/14
First scenario, consider a clinical trial, where we randomize individuals independently (by individual coin flip, say) to treatment or control groups.
What if we re-randomize until the two groups have exactly the same size?
2/14
This is the same as choosing an equal split uniformly randomly, and standard statistical approaches will still apply without problems.
It is similar if one re-randomized until the populations were within some threshold, or if this was true for subgroups (sex, race, etc).
3/14
We carried out simple analyses of this data using standard nonparametric paired statistical tests.
Through this lens, there are highly significant effects on behavior, but the primary outcome (symptomatic seropositivity) is not significant. 2/..
One of the striking things in the figure above is the imbalance in the size of the consenting populations between the (50:50 randomized) treatment/control groups. There are highly nonrandom differences in the rates consent staff reached households in treatment/control villages.
The authors of this study are doubling down on @Jabaluck's "guesstimate" of the # of lives masks would save in the U.S. based on their (impressive, sorely needed) cluster trial in Bangladesh.
There are many reasons this guess is speculative to the point of not being useful. 1/6
Apart from the obvious things about translating a finding which was highly context specific (the benefit concentrated in particular ages and in villages using a particular mask type) to a completely different context, one cannot make these estimates ignoring epidemic dyanmics. 2/
E.g.:
Suppose every K infections results in death.
To a first approximation, to believe "masks for a year" prevents x deaths, you either have to believe that it increases by K*x the number of people who will never get infected, or the number who would get vaccinated first.
3/
Everyone should look at the remarkable work done in this cluster randomized trial.
They found that an intervention which increased surgical mask uptake in community settings significantly reduced SARS-CoV-2 infection among older adults.
Most people's 1st tendency is to claim studies like this support what they already knew. In reality, the study had specific and not necessarily intuitive findings.
The study even collected predictions from experts, and found that they failed to predict the study outcomes!
2/6
For example, the study found that increasing mask usage had statistically significant effect on SARS-CoV-2 infection.
But these results were driven by surgical mask use, and by reductions in infections in people over 50.
A brief reminder that CDC mask guidelines start at age 2.
At the time of this writing, the MMWR with data had 74 retweets and the MMWR on that one time this one crazy thing happened had 1.1K retweets, many from serious people claiming that this report affected in some fundamental way our understanding of COVID-19 risk in schools. 2/
Data is boring and stories seem compelling. But scientists and public health agencies should be actively working against the natural tendency to give greater weight to outlier incidents than data-driven understanding of risks.
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