What leftists hope you won't find out as Biden claims a "COVID miracle"
- Thread starter Legion
- Start date
Darth Omar
Russian asset
But that doesn’t negate the fact that Biden ‘got lucky’ by coming on board at the same time they decided it might be a good idea to stop over cycling PCR tests.
Funny, how that works.
How is a political party "working hard to fight" a pandemic?
Has any pandemic in history ever been ended by a political party, or a government?
Just a coincidence, lol.
Didn't the CDC "coincidentally" update their advice for diagnosing COVID cases and categorizing deaths at around the same time, lol?
Will you understand if he doesn't, Cap'n?
cancel2 2022
Canceled
.
That's been known about for quite some time. Kary Mullis, the inventor of the test warned of its limitations but sadly he was largely ignored Covid testing.
That's been known about for quite some time. Kary Mullis, the inventor of the test warned of its limitations but sadly he was largely ignored Covid testing.
Indeed, and one of senile Joe's hacks admitted that the pandemic was the "best thing that ever happened".
https://www.theguardian.com/us-news...a-dunn-lucky-book-jonathan-allen-annie-parnes
No evidence exists that governments can protect anyone from the China Virus
As we near the one-year anniversary of Fauci's fatuous "fifteen days to flatten the curve", we have yet to see any data suggesting that the past year of politicized panic propaganda, lockdowns and mask mandates has done anything to keep us safe from the Chinese disease.
Notice anything about the date when alleged "COVID" hospitalizations began to decline?
More at link: https://mises.org/wire/almost-year-later-theres-still-no-evidence-showing-governments-can-control-spread-covid-19
As we near the one-year anniversary of Fauci's fatuous "fifteen days to flatten the curve", we have yet to see any data suggesting that the past year of politicized panic propaganda, lockdowns and mask mandates has done anything to keep us safe from the Chinese disease.
Notice anything about the date when alleged "COVID" hospitalizations began to decline?
More at link: https://mises.org/wire/almost-year-later-theres-still-no-evidence-showing-governments-can-control-spread-covid-19
New COVID-19 cases, hospitalizations, and deaths all dropped last month as the predictable result of the politicized WHO and the DEMOCRAT-controlled CDC "updating" their "guidance" to make it look like the fake president is "eradicating" the Chinese disease.
Nevertheless, DEMOCRAT politicians and bureaucrats have not abated their foolish fear-mongering.
The idiotic CDC even called for “double-masking” and wrapping pantyhose around your head.
https://www.nytimes.com/2021/02/10/health/coronavirus-masks-cdc.html
Nevertheless, DEMOCRAT politicians and bureaucrats have not abated their foolish fear-mongering.
The idiotic CDC even called for “double-masking” and wrapping pantyhose around your head.
https://www.nytimes.com/2021/02/10/health/coronavirus-masks-cdc.html
I got my first vaccine Wed. There were long lines, many people administering shots. They had people with computers making the appointments for the second shot before I left. There were lines all day long, 6 days a week. There were even people on line wearing MAGA hats.
Biden has procured the vaccines and organized delivery. It sure is pleasant to see the government working again. Trump and rightys sabotage the government, As an example, the Trump virus is getting under control so righty governors opened up and took away mask and distancing requirements.
So you are, as usual, bigly wrong and should be ashamed and shut the fuck up.
The aim of science is to establish facts, as accurately as possible.
It is therefore crucially important to determine whether an observed phenomenon is real, or whether it’s the result of pure chance. If you declare that you’ve discovered something when in fact it’s just random, that’s called a false discovery or a false positive. And false positives are alarmingly common in some areas of medical science.
In 2005, the epidemiologist John Ioannidis at Stanford caused a storm when he wrote the paper ‘Why Most Published Research Findings Are False’, focusing on results in certain areas of biomedicine. He’s been vindicated by subsequent investigations. For example, a recent article found that repeating 100 different results in experimental psychology confirmed the original conclusions in only 38 per cent of cases.
The problem of how to distinguish a genuine observation from random chance is a very old one. It’s been debated for centuries by philosophers and, more fruitfully, by statisticians. It turns on the distinction between induction and deduction. Science is an exercise in inductive reasoning: we are making observations and trying to infer general rules from them. Induction can never be certain. In contrast, deductive reasoning is easier: you deduce what you would expect to observe if some general rule were true and then compare it with what you actually see. The problem is that, for a scientist, deductive arguments don’t directly answer the question that you want to ask.
What matters to a scientific observer is how often you’ll be wrong if you claim that an effect is real, rather than being merely random. That’s a question of induction, so it’s hard. In the early 20th century, it became the custom to avoid induction, by changing the question into one that used only deductive reasoning. In the 1920s, the statistician Ronald Fisher did this by advocating tests of statistical significance. These are wholly deductive and so sidestep the philosophical problems of induction.
Tests of statistical significance proceed by calculating the probability of making our observations (or the more extreme ones) if there were no real effect. This isn’t an assertion that there is no real effect, but rather a calculation of what would be expected if there were no real effect. The postulate that there is no real effect is called the null hypothesis, and the probability is called the p-value. Clearly the smaller the p-value, the less plausible the null hypothesis, so the more likely it is that there is, in fact, a real effect. All you have to do is to decide how small the p-value must be before you declare that you’ve made a discovery. But that turns out to be very difficult.
The problem is that the p-value gives the right answer to the wrong question. What we really want to know is not the probability of the observations given a hypothesis about the existence of a real effect, but rather the probability that there is a real effect – that the hypothesis is true – given the observations. And that is a problem of induction.
Confusion between these two quite different probabilities lies at the heart of why p-values are so often misinterpreted. It’s called the error of the transposed conditional. Even quite respectable sources will tell you that the p-value is the probability that your observations occurred by chance. And that is plain wrong.
More @ link: https://aeon.co/essays/it-s-time-for-science-to-abandon-the-term-statistically-significant
It is therefore crucially important to determine whether an observed phenomenon is real, or whether it’s the result of pure chance. If you declare that you’ve discovered something when in fact it’s just random, that’s called a false discovery or a false positive. And false positives are alarmingly common in some areas of medical science.
In 2005, the epidemiologist John Ioannidis at Stanford caused a storm when he wrote the paper ‘Why Most Published Research Findings Are False’, focusing on results in certain areas of biomedicine. He’s been vindicated by subsequent investigations. For example, a recent article found that repeating 100 different results in experimental psychology confirmed the original conclusions in only 38 per cent of cases.
The problem of how to distinguish a genuine observation from random chance is a very old one. It’s been debated for centuries by philosophers and, more fruitfully, by statisticians. It turns on the distinction between induction and deduction. Science is an exercise in inductive reasoning: we are making observations and trying to infer general rules from them. Induction can never be certain. In contrast, deductive reasoning is easier: you deduce what you would expect to observe if some general rule were true and then compare it with what you actually see. The problem is that, for a scientist, deductive arguments don’t directly answer the question that you want to ask.
What matters to a scientific observer is how often you’ll be wrong if you claim that an effect is real, rather than being merely random. That’s a question of induction, so it’s hard. In the early 20th century, it became the custom to avoid induction, by changing the question into one that used only deductive reasoning. In the 1920s, the statistician Ronald Fisher did this by advocating tests of statistical significance. These are wholly deductive and so sidestep the philosophical problems of induction.
Tests of statistical significance proceed by calculating the probability of making our observations (or the more extreme ones) if there were no real effect. This isn’t an assertion that there is no real effect, but rather a calculation of what would be expected if there were no real effect. The postulate that there is no real effect is called the null hypothesis, and the probability is called the p-value. Clearly the smaller the p-value, the less plausible the null hypothesis, so the more likely it is that there is, in fact, a real effect. All you have to do is to decide how small the p-value must be before you declare that you’ve made a discovery. But that turns out to be very difficult.
The problem is that the p-value gives the right answer to the wrong question. What we really want to know is not the probability of the observations given a hypothesis about the existence of a real effect, but rather the probability that there is a real effect – that the hypothesis is true – given the observations. And that is a problem of induction.
Confusion between these two quite different probabilities lies at the heart of why p-values are so often misinterpreted. It’s called the error of the transposed conditional. Even quite respectable sources will tell you that the p-value is the probability that your observations occurred by chance. And that is plain wrong.
More @ link: https://aeon.co/essays/it-s-time-for-science-to-abandon-the-term-statistically-significant
You can thank Trump for that. Not Biden. And as usual you cannot back up any of your constant bullshit with fact. None.
Poopeye is back with his nonsense.
Lets see if Rule 14 is real.
Poopeye, you're subject to the same rule also, you also troll others, don't forget.
Maybe not, Skipper.
Trump? What for? He has you so conned any trace of logic and sense are impossible. Oh year, his genius saved us from a hoax. A disease that would never come here that he had been perfectly handled. It is sad that you have gotten so deep in lies that facts do not exist for you.
Name one policy or directive biden gave for Covid. Now would good. Otherwise just STFU.