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Originally Posted by Bowser
LOL. *looks at Donger, points at my crotch*
I will own that I took that video to mean something more than it was, possibly. Maybe even go as far as falling for some confirmation bias I found in the words of those two doctors. It's clear that more testing needs to be done before we "conclusively" can state numbers on the scale they are suggesting.
However, if their lead detractor Dr. Carl Bergstrom of the University of Washington has anything else to say than "They've used methods that are ludicrous to get results that are completely implausible" I would be all ears. In fact, I think he really should explain how he came to that conclusion, especially in the face of what's being accused of the two doctors.
Also, let the record show that the Orange County Register is the one that took it political by pointing out how the two doctors were going on to Laura Ingraham's show, and how their story were making the rounds on "right wing media". Additionally, the article goes on to allege the two doctors are in this for fame and fortune, but never really shows WHY what they're saying is indeed not factual outside the lack of more wide spread testing.
Not sure if these two are 100% correct or incorrect, but it sure is telling how the media subtly tries to paint them in a certain light.
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I can tell you why they're wrong:
They implemented selection bias to draw their conclusions. Those most likely to go to a coronavirus clinic are those most likely to test positive for COVID. Without random sampling of the public (which is done for other diseases, mind you), inferences I draw from that information are inherently flawed. They claimed that because 6.6% of their patients were positive for COVID, that 12% of the population in California was positive.
Let's say that I wanted to test for the prevalence of black lung in the country as a whole. Should I test people in the Pacific Northwest, or people in West Virginia? What are the flaws of doing one vs. the other? In reality, I should randomly sample enough of the population for a representative sample, not just one group.
If I test nothing but coal miners, what conclusions might I draw from the severity of black lung that are incorrect?
We also know from hard data in New York that the IFR is far above the lower bounds of their conclusions anyway, because more than 0.1% of the entire population in NYC has already died of COVID-19, and that's with far less than 100% of the city being infected.
I can tell you: they are 100% incorrect.