There was a popular meme going around social media sites awhile back usually entitled "Correlation does not Equal Causation." In the most common photography I saw, two seagulls are resting on a metal pole, with one pole bent down into a V shape in the exact spot where the seagull landed. The obvious conclusion one draws is that the seagull did not cause the pole to bend. Sometimes this meme was repeated with photos of cats on bent or mishappen roofs. It was usually posted by someone who was pro Covid vax, who was trying to explain away concerns about deaths or side effects after the jab.
It had me recently pondering how we do draw conclusions about cause and effect in natural datasets. Bradford-Hill criteria are used to support a probably causal association. I'm going to boil it down to six parts:
Plausibility (there's an explanation for how the observance and event could be related)
Consistency (you see the same thing happening in multiple places)
Temporality (the event happens shortly after the exposure)
Strength (the event has many cases strongly correlated with the exposure)
Specificity (the only thing that changed is the exposure)
Change in risk factor (the event stops happening if the exposure is not there)
So let's look at that meme again and explain it.
Observation: The seagull seems to be associated with a bent steel pole.
Plausibility? No. There is no reasonable explanation for why a lightweight bird landing on a heavy duty steel pole would cause it to bend.
Consistency? No. Consistency isn't even shown in the picture, as there is another seagull on a pole that is not bent.
Temporality? Not possible to tell from the picture, but unlikely. For that to be true the pole would have had to bend like that immediately or shortly after the seagull landed on it. If the pole was bent before the seagull landed, as was likely the case, then this is solidly refuted.
Strength? No. We'd need a large dataset of seagulls on bent poles for this. A single picture anecdote doesn't have it.
Specificity? No. Lots of other things could have caused the pole to bend, such as rust, weather, or a large item such as a car hitting it.
Change in risk factor? No. Would steel poles stop bending if seagulls just stopped landing on them?
Now let's try using the same criteria for the Covid vaccines.
Observation: Covid vaccines seem to be associated with death and injury.
Plausibility? Yes. There are many mechanisms by which a novel delivery of an untested, expirimental Gene therapy, rushed to market with lax safety protocols and no liability for the manufacturers, in part funded by organizations that want the world population reduced, might potentially kill or injure people.
Consistency? Yes. There have been reports from all over the world, from people who have taken all of the major vaccine brands, of death and injury.
Temporality? Yes. Many deaths are reported within a few days of the jab, with many being injured, reporting side effects or being hospitalized eventually leading to death within hours or even minutes of the shots.
Strength? Yes. Now the CDC has done some fancy statistical manuvering so that there is never any power in the VAERS safety signal, but limited datasets with a known numerator and denominator have shown statistical significance. This is not to say that everyone who takes the jab gets seriously injured or dies. But even a 1 in 1000 chance is a pretty mass genocide against a disease with a very high survival rate in healthy people.
Specificity? Most likely, but the hardest to prove. Adverse reactions to the vaccines can be blamed on Covid, long haul Covid, Post Pandemic Stress disorder and other things also around in 2020. Atheletes collapsing and dying has been blamed on all of those things plus climate change, dehydration, overexertion and other nonsense. But the only thing that changed specifically in 2021 is the rollout of the mass vaccine campaign. That's very specific.
Change in risk factor? Yes. It's hard to say what health problems might pop up in the future possibly from the Covid vax even if the mass jab program were suspended worldwide immediately. But it is known that places that vaccinated less don't have the high level of Covid outbreaks. All cause mortality also remains very high in highly vaccinated countries.
Mostly I think the meme was just another excuse to shut down critical thinking skills...
Brilliant! Thank you. Temporality is sometimes referred to as Granger Causality esp. in Time Series Analysis