Anti-vaxx Chronicles
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Anti-vaxx Chronicles
Yeah Dr Angus never brought any evidence. Never said that mRNA vaccines were unsafe all he said he noticed discrepancies thus more studies need to be done.
He failed to mention that more Studies have already been done. I betcha no matter how many studies fail to validate his hypothesis, he'll always ask for more but will never conduct them himselve. Or God forbid publish.
Time to bone up on correlation versus casual effect
He failed to mention that more Studies have already been done. I betcha no matter how many studies fail to validate his hypothesis, he'll always ask for more but will never conduct them himselve. Or God forbid publish.
Time to bone up on correlation versus casual effect
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Correlation vs. Causation
Highlighted text are my own.
https://www.scribbr.com/methodology/cor ... causation/
Correlation vs. Causation | Difference, Designs & Examples
Correlation means there is a statistical association between variables. Causation means that a change in one variable causes a change in another variable.
In research, you might have come across the phrase “correlation doesn’t imply causation.” Correlation and causation are two related ideas, but understanding their differences will help you critically evaluate sources and interpret scientific research.
What’s the difference?
Correlation describes an association between types of variables: when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables. These variables change together: they covary. But this covariation isn’t necessarily due to a direct or indirect causal link.
Causation means that changes in one variable brings about changes in the other; there is a cause-and-effect relationship between variables. The two variables are correlated with each other and there is also a causal link between them.
A correlation doesn’t imply causation, but causation always implies correlation.
Why doesn’t correlation mean causation?
There are two main reasons why correlation isn’t causation. These problems are important to identify for drawing sound scientific conclusions from research.
The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not. For example, ice cream sales and violent crime rates are closely correlated, but they are not causally linked with each other. Instead, hot temperatures, a third variable, affects both variables separately. Failing to account for third variables can lead research biases to creep into your work.
The directionality problem occurs when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other. For example, vitamin D levels are correlated with depression, but it’s not clear whether low vitamin D causes depression, or whether depression causes reduced vitamin D intake.
You’ll need to use an appropriate research design to distinguish between correlational and causal relationships:
Correlational research designs can only demonstrate correlational links between variables. Experimental designs can test causation.
Correlational research
In a correlational research design, you collect data on your variables without manipulating them.
Example: Correlational researchYou collect survey data to investigate whether there is a relationship between physical activity levels and self esteem. You ask participants about their current levels of exercise and measure their self-esteem using an inventory.
You find that physical activity level is positively correlated with self esteem: lower levels of physical activity are associated with lower self esteem, while higher levels of physical activity are associated with higher self esteem.
Correlational research is usually high in external validity, so you can generalize your findings to real life settings. But these studies are low in internal validity, which makes it difficult to causally connect changes in one variable to changes in the other.
These research designs are commonly used when it’s unethical, too costly, or too difficult to perform controlled experiments. They are also used to study relationships that aren’t expected to be causal.
Example: Correlational researchTo study whether consuming violent media is related to aggression, you collect data on children’s video game use and their behavioral tendencies. You ask parents to report the number of weekly hours their child spent playing violent video games, and you survey parents and teachers on the children’s behaviors.
You find a positive correlation between the variables: children who spend more time playing violent video games have higher rates of aggressive behavior.
Third variable problem
Without controlled experiments, it’s hard to say whether it was the variable you’re interested in that caused changes in another variable. Extraneous variables are any third variable or omitted variable other than your variables of interest that could affect your results.
Limited control in correlational research means that extraneous or confounding variables serve as alternative explanations for the results. Confounding variables can make it seem as though a correlational relationship is causal when it isn’t.
Example: Extraneous and confounding variablesIn your study on violent video games and aggression, parental attention is a confounding variable that could influence how much children use violent video games and their behavioral tendencies. Low quality parental attention can increase both violent video game use and aggressive behaviors in children.
But it’s not something you control for, so you can only draw a conclusion of correlation between your main variables.
When two variables are correlated, all you can say is that changes in one variable occur alongside changes in the other.
...
Continue reading at: https://www.scribbr.com/methodology/cor ... causation/
https://www.scribbr.com/methodology/cor ... causation/
Correlation vs. Causation | Difference, Designs & Examples
Correlation means there is a statistical association between variables. Causation means that a change in one variable causes a change in another variable.
In research, you might have come across the phrase “correlation doesn’t imply causation.” Correlation and causation are two related ideas, but understanding their differences will help you critically evaluate sources and interpret scientific research.
What’s the difference?
Correlation describes an association between types of variables: when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables. These variables change together: they covary. But this covariation isn’t necessarily due to a direct or indirect causal link.
Causation means that changes in one variable brings about changes in the other; there is a cause-and-effect relationship between variables. The two variables are correlated with each other and there is also a causal link between them.
A correlation doesn’t imply causation, but causation always implies correlation.
Why doesn’t correlation mean causation?
There are two main reasons why correlation isn’t causation. These problems are important to identify for drawing sound scientific conclusions from research.
The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not. For example, ice cream sales and violent crime rates are closely correlated, but they are not causally linked with each other. Instead, hot temperatures, a third variable, affects both variables separately. Failing to account for third variables can lead research biases to creep into your work.
The directionality problem occurs when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other. For example, vitamin D levels are correlated with depression, but it’s not clear whether low vitamin D causes depression, or whether depression causes reduced vitamin D intake.
You’ll need to use an appropriate research design to distinguish between correlational and causal relationships:
Correlational research designs can only demonstrate correlational links between variables. Experimental designs can test causation.
Correlational research
In a correlational research design, you collect data on your variables without manipulating them.
Example: Correlational researchYou collect survey data to investigate whether there is a relationship between physical activity levels and self esteem. You ask participants about their current levels of exercise and measure their self-esteem using an inventory.
You find that physical activity level is positively correlated with self esteem: lower levels of physical activity are associated with lower self esteem, while higher levels of physical activity are associated with higher self esteem.
Correlational research is usually high in external validity, so you can generalize your findings to real life settings. But these studies are low in internal validity, which makes it difficult to causally connect changes in one variable to changes in the other.
These research designs are commonly used when it’s unethical, too costly, or too difficult to perform controlled experiments. They are also used to study relationships that aren’t expected to be causal.
Example: Correlational researchTo study whether consuming violent media is related to aggression, you collect data on children’s video game use and their behavioral tendencies. You ask parents to report the number of weekly hours their child spent playing violent video games, and you survey parents and teachers on the children’s behaviors.
You find a positive correlation between the variables: children who spend more time playing violent video games have higher rates of aggressive behavior.
Third variable problem
Without controlled experiments, it’s hard to say whether it was the variable you’re interested in that caused changes in another variable. Extraneous variables are any third variable or omitted variable other than your variables of interest that could affect your results.
Limited control in correlational research means that extraneous or confounding variables serve as alternative explanations for the results. Confounding variables can make it seem as though a correlational relationship is causal when it isn’t.
Example: Extraneous and confounding variablesIn your study on violent video games and aggression, parental attention is a confounding variable that could influence how much children use violent video games and their behavioral tendencies. Low quality parental attention can increase both violent video game use and aggressive behaviors in children.
But it’s not something you control for, so you can only draw a conclusion of correlation between your main variables.
When two variables are correlated, all you can say is that changes in one variable occur alongside changes in the other.
...
Continue reading at: https://www.scribbr.com/methodology/cor ... causation/
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blah, blah, blah.
All previously "misinformation"
times a bitch
2 more years
- People with weakened immune systems may have a reduced immune response to COMIRNATY
- COMIRNATY may not protect all vaccine recipients
- Authorized or approved mRNA COVID-19 vaccines show increased risks of myocarditis (inflammation of the heart muscle) and pericarditis (inflammation of the lining outside the heart), particularly within the first week following vaccination. For COMIRNATY, the observed risk is highest in males 12 through 17 years of age.
All previously "misinformation"
times a bitch
2 more years
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So I hear you saying more studies were done.
You accidentally left out the part of the quote, The chance of having this occur is low.
Consuming Peanuts may cause severe reactions even death.
Therefor we should stop all sales of peanutbutter..
So you also agree with Pfizer also that it's MRNA vaccine is overwhelming safe and effective with notable minor exceptions?
You accidentally left out the part of the quote, The chance of having this occur is low.
Consuming Peanuts may cause severe reactions even death.
Therefor we should stop all sales of peanutbutter..
So you also agree with Pfizer also that it's MRNA vaccine is overwhelming safe and effective with notable minor exceptions?
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In comparison to other vaccines, this one seems to have been the least of both claims.
There seems to be new studies published regularly, and this should continue for at least 2 more years.
Hasn't this vaccine received more VAERS reports than any other?
Hasn't this vaccine received more warnings from substantial members of the medical community than any other?
Has anyone lost their job or reputation for refusing to eat peanut butter?
There seems to be new studies published regularly, and this should continue for at least 2 more years.
Hasn't this vaccine received more VAERS reports than any other?
Hasn't this vaccine received more warnings from substantial members of the medical community than any other?
Has anyone lost their job or reputation for refusing to eat peanut butter?
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Anti-vaxx Chronicles
-Johns Hopkins Bloomberg School of Public HealthWhat VAERS Is (And Isn’t)
The public database of reported post-vaccination health issues is often misused to sow misinformation...
VAERS cannot determine whether an adverse event was caused by a vaccination..
Since the emergence of COVID-19 vaccines, however, the database has garnered more dubious notoriety.
Anti-vaccination fringe groups have attempted to spin false stories using VAERS data,
adding to misinformation about the safety of COVID-19 vaccinations...
VAERS is a publicly available, searchable database of reports that have not been verified.
It simply contains whatever people have voluntarily reported...
It’s very open and public and searchable.
Since it’s so transparent, people don’t really understand what it’s for.
They think it’s things that are vetted and have causal relationships with the vaccine...
The COVID vaccine especially is where VAERS has gotten so misused,
Eighty percent of people in this country have gotten at least one dose.
Well, a lot of things have happened to 80% of people in the last two years that are unrelated to the vaccine...
Prohibition is Futile THC will be Assimilated
~ ~ ~ ~ ~ ~ ~ How To Pass Drug Tests ~ ~ ~ ~ ~ ~ ~ ~
Reality is merely an illusion, albeit a very persistent one. -Albert Einstein
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Reality is merely an illusion, albeit a very persistent one. -Albert Einstein
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On misinterpreting VAERS data, see previous post on Correlation vs. Causation. Or just let Spock explain
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https://twitter.com/JohnMappin/status/1 ... 3643267337
Big news out of New Zealand.
No other proof than a whistleblower, but certainly worth watching.
If 30 people who all got jabbed on same day, all died, that is certainly a concern.
Big news out of New Zealand.
No other proof than a whistleblower, but certainly worth watching.
If 30 people who all got jabbed on same day, all died, that is certainly a concern.
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"Tens of thousands of deaths linked to the Jab"
Well that aint right. But a mathematician well versed in statistical analysis said so. Oh My! But they won't release the data Oh My!
Well that aint right. But a mathematician well versed in statistical analysis said so. Oh My! But they won't release the data Oh My!