bullshit – n. language, statistical figures, data, graphics and other forms of presentation that are intended to persuade by impressing and overwhelming a reader or listener with a blatant disregard for truth or logical coherence
The definition above comes from Carl Bergstrom, professor of biology at the University of Washington. In an interview with The Guardian, Bergstrom discusses his interest in infectious diseases and networked information. The novel coronavirus pandemic has unleashed an “infodemic” of hoaxes and conspiracy theories surrounding the virus’ behavior and treatments.
Bergstrom says of his definition, “The idea with bullshit is that it’s trying to appear authoritative and definitive in a way that’s not about communicating accurately and informing a reader, but rather by overwhelming them, persuading them, impressing them. If that’s done without any allegiance to truth, or accuracy, that becomes bullshit.”
Statistics become ripe for exploitation because so few among the general public feel qualified to question misinformation presented as data. Where COVID-19 presents a unique problem is we are not accustomed to data being as politicized as it has in the pandemic.
Bad actors take good information and twist it for their own ends. Plus, in presenting scientific data researchers typically don’t spend half a page discussing the limitations of their models, Bergstrom says:
We’re used to writing for an audience of 50 people in the world, if we’re lucky, who have backgrounds that are very similar to our own and have a huge set of shared assumptions and shared knowledge. And it works really well when you’re writing on something that only 50 people in the world care about and all of them have comparable training, but it is a real mess when it becomes pressing, and I don’t think any of us have figured out exactly what to do about that because we’re also trying to work quickly and it’s important to get this information out.
Bergstrom worries that some studies are performed “with an implicitly political context, depending on who the funders are or what the orientations and biases of some of the researchers.” Then there are the problems of selection bias. He cites the Santa Clara antibody (serology) study in which the method of recruiting test subjects may have skewed results. Plus the error rate in sampling can render results erroneous. He cites Santa Clara again:
If you have a test that has a 3% error rate, and the incidence in the population is below 3%, then most of the positives that you get are going to be false positives. And so you’re not going to get a very tight estimate about how many people have it. This has been a real problem with the Santa Clara study. From my read of the paper, their data is actually consistent with nobody being infected. A New York City study on the other hand showed 21% seropositive, so even if there has a 3% error rate, the majority of those positives have to be true positives.
It’s important to realize how scientific models released during the pandemic change people’s behavior, and those changes feed back into and alter subsequent results. Our models “often don’t treat that part explicitly,” Bergstrom cautions.
One source of confusion that continues to irritate me (and that the press has yet to clarify) is what people mean by tests. The Trump administration, governors, and the press use the same word to mean many different things.
Donald Trump recently called out Maryland Gov. Larry Hogan (R) for complaining he didn’t have enough tests. But look here, Trump said, showing off a map of Maryland. Look at all these testing facilities and all the machine that are underutilized. There is excess testing capacity across the country, the administration claims.
Meanwhile, governors complain they lack sufficient individual testing kits and sampling supplies needed to send in samples for analysis in the quantities needed to rein in the contagion. Each suggests the other is talking bullshit when they are in fact talking past each other.
Plus, there are diagnostic tests that use nasal swabs to check for active COVID-19 infections. There are serology tests that use blood samples to check for antibodies left behind in patients who have recovered. But the administration, the states, and the press are doing a lousy job of clarifying where we stand by using the word tests for everything from kits to labs to machines to supplies to different types of testing. Nothing is clear because nothing is clear. And some of it is simply bullshit.
Bergstrom co-wrote “Calling Bullshit” to be released this summer. We need to relearn how to question information presented to us. The blurb from Penguin Random House explains, “Startup culture elevates bullshit to high art. We are fairly well equipped to spot the sort of old-school bullshit that is based in fancy rhetoric and weasel words, but most of us don’t feel qualified to challenge the avalanche of new-school bullshit presented in the language of math, science, or statistics.”
This environment is fertile ground for conspiracy theories. What conspiracy theorists lack in quality they make up for in quantity. Debunk the top item in their tall stacks of “evidence” and it’s quickly discarded. But what about this? they ask. Debunk that and they move on to the next. Lather, rinse, repeat. We are to be impressed with the sheer volume of bad or misinterpreted data. No amount of debunking with counter-evidence will dent belief. They see smoke, so there must be a fire. In fact, they are the ones flinging smoke bombs.
Correction: Misidentified Hogan as a Dem. [h/t TE]
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For The Win, 3rd Edition is ready for download. Request a copy of my free countywide GOTV mechanics guide at ForTheWin.us. This is what winning looks like.
Note: The pandemic will upend standard field tactics in 2020. If enough promising “improvisations” come my way by June, perhaps I can issue a COVID-19 supplement.