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1387155 Posts in 66522 Topics- by 59076 Members - Latest Member: kaikecarlos

January 21, 2021, 01:39:25 PM

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J-Snake
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« Reply #40 on: November 14, 2020, 04:29:04 PM »

The "correlation doesn't imply causation" argument doesn't really work here since there's a clear cause and effect link between the two variables in question.
I know that it all makes perfect sense in your head, but that does not reflect, or account for the pitfalls of how data is collected. For example, there is a possibility that people tend to get more ill in the winter in general, and it might have an impact on tests because they are not perfect. Also, even when there is a clear cause and effect link, you still have to know how strong it is to get meaningful information. Choose a number. The more you test, the more infected you get.

Apparently an overly aggressive response is a problem in particular with covid because it's in your lungs?
Imagine you have several different protection systems but most of them don't work well, then the one that does tries to compensate for them by working in an overdrive, causing an overreaction. In this case it can be fatal because it stops the flow of oxygen.
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J-Snake
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« Reply #41 on: November 14, 2020, 05:54:52 PM »

Just for clarity to raise awareness how quickly one can jump to seemingly sound conclusions:

Any significant spike in cases that leads to community spread will cause an increase in both total cases numbers and seriously ill numbers since it will eventually spread across age/risk groups.
There are two problems with this statement.

The first one is is concerned with propositional logic. Here it is assumed that just because A (more infected in reality) implies B (more infected "in data"), B has to imply A, which is false (tests are not perfect plus other problems with data gathering can be at play).

The second one is the "blind faith" in an assumed particular relation between the infected vs. ill data sets. You can only establish the weight of a causal link with a sufficient amount of data and the elimination of correlational factors (which, if a person understands one or two things about statistics knows is a hard challenge). If you only test the ill the weight will be high, if you only test the "just infected" the weight will be low.
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« Reply #42 on: November 16, 2020, 07:55:53 AM »

J-Snake, do you realize that you're arguing that 1) an increase in test numbers doesn't correlate to an increase in covid cases, and 2) an increase in covid cases doesn't correlate to an increase in seriously ill cases? About a virus that has literally caused a pandemic??

I'd be willing to entertain the possibility if it weren't for the fact that case number surveys across countries have consistently established a correlation between case numbers and hospitalizations. The weight of the correlation isn't always the same, sure, but it's always significant. If there wasn't a significant risk of ending up in the hospital from catching covid, public health officials wouldn't be advocating for masks and distancing.

I don't know what the hell the point of your argument is, but it certainly seems dangerous to push it, considering the amount of people who are denying the very existence of the pandemic. Would you be willing to elaborate on your intentions?
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