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Most of my 25-year tech career has been as a specialist in databases–programming, administering, and analyzing data. One thing I’ve learned is that a good analyst will let the data guide them to answers. In other words, you start in a neutral position and have the data push you to the correct conclusions. However, bad data analysts do the exact opposite. They start with a theory or conclusion, then look for data to prove it. This is always dangerous because it’s easy to change sample sizes, assumptions, base comparisons, and interpretations until you get the answers you want.
The Covid debacle of the last two years has been a textbook case of the latter. One example: when the initial death & hospitalizations rates were too low to stoke the proper amount of fear, they changed the definitions to hospitalized or died with Covid instead of because of Covid. In other words, if you were in a car accident and later died because of the injuries, you’d still be thrown in the total if you happen to test positive while in the hospital. Another example: when vaccines failed to slow the case rates or stop the spread, Fauci and the CDC changed their story to say the vaccines prevented severe cases and that the hospitalizations were a major problem only of the unvaccinated. But dig deeper–CDC director, Rochelle Walensky, admitted the hospitalization rates they cited included all cases going back to January, before vaccines were readily available. In other words, since everyone was unvaccinated, all hospitalizations were thrown in the “unvaccinated” counts, and don’t forget the difference between hospitalization with Covid vs because of Covid. Walensky also admitted that a person was still considered unvaccinated during the 14-day period after the 2nd shot. This had a double bonus of hiding any injuries from the vaccine itself. In other words, if you suffer a severe reaction to the vaccine that puts you in the hospital, they throw you in the “unvaccinated Covid hospitalization” group. Now, as more information comes out on Covid spread throughout heavily-vaxxed communities, are they letting the data guide them to new conclusions? No. They’re changing the assumptions and definitions again so the “new data” backs up what they’re trying to prove. “Fully vaccinated” is now changing to only those with a 3rd shot. Thus, people who’ve only received two shots can be thrown into the “unvaccinated” group, and a whole new line of BS can be fed to a lapdog media.
A good scientist or data analyst can see these obvious manipulations, and countless ones who have no interest in politics have tried to point these facts out. But what happens when they do–they’re censored! Posts are removed. Voices are de-platformed. The media throws them in the nutcase “misinformation”-spreading conspiracy theorist group.
When you see the censorship and manipulation of data on vaccines the past year, it’s fair to wonder what real data shows on many proposed Covid remedies such as Ivermectin and Hydroxychloroquine. Fauci wanted to protect his almighty vaccines, so what assumptions, samples, and definitions were changed in the data to reach the conclusion he wanted? The answers are out there, but it’s a battle every day to fight thru the Big Tech Thought Police stranglehold of information.
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4 thoughts on “12-29 Politically Incorrect Daily”
If I remember correctly, the first computer model used to ‘predict’ the rate of spread of the virus was the same one that was used on the mad cow outbreak in England some years ago. It was proven wrong back then, but it gave figures that could be, and were, used to scare the public. Science? Bah humbug!
You are spot on with your random thoughts on research and analysis. I wish politicians and “scientists” would quit starting with a conclusion/agenda and manipulating data to “prove” their foregone conclusion.
Kudos to you, Joe, for pointing out what should be obvious to all—even liberals.
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