Okay, so let’s breakdown what Steve did here. He said:
“If SIDS is just randomly happening to babies, and babies are vaccinated every 60 days like clockwork, the chance of a SIDS death happening within any 48 hour window post-vaccine is 1/30.”
Hmm. Babies are vaccinated every 60 days like clockwork. How many babies? How can you assess the …
Okay, so let’s breakdown what Steve did here. He said:
“If SIDS is just randomly happening to babies, and babies are vaccinated every 60 days like clockwork, the chance of a SIDS death happening within any 48 hour window post-vaccine is 1/30.”
Hmm. Babies are vaccinated every 60 days like clockwork. How many babies? How can you assess the probability of a random babies chance of SIDS without knowing how many babies were vaccinated during any 60 day interval? You can’t. “Within any 48 hour window post- vaccine is 1/30th”. With this information you cannot determine what the rate of SIDS is. The rate of SIDS is = (the number of SIDS cases)/(total number vaccinated). It is not = (2 days)/(60 days). That is not a rate. It’s a dimensionless quantity. This is the problem. We only know how many SIDS cases there were spread out over 7 years and how many fell within a 2 day interval after vaccination. But it gets more convoluted because there aren’t 30 2 day intervals during a 60 day period as the vaccinations are made every day so there are 60 2 day periods with the last 2 day interval spanning days 60 to 61. But that does not matter. We are missing a crucial number here. We do not know what the rate of SIDS is because we do not know how many babies were vaccinated over that 60 day period.
Steve goes on to say:
“So if there are 300 babies who died of SIDS, we’d expect that 10 of them, on average, would happen within every 48 hour window post vaccine.”
If the 300 events and the 225 events that fall within the 2 day parameter are randomly distributed then we must spread them out over 7 years of overlapping 2 day intervals. Let’s work this out. There were 300 total events over 7 years 225 of those events occurred within a 2-day interval. Over 7 years there are 2556 possible 2-day intervals. If the 225 events are randomly distributed over the 2556 intervals we need each interval has an equal chance of containing 1 of the 225 events so to calculate the expected number per interval there were a total events = 225 and a total of intervals = 2556 so the expected number = Total events / Total intervals make the expected number = 225 / 2556 or the expected number = 0.088 events per interval. Therefore, if the 225 events that occurred in a 2-day interval are randomly distributed over the 2556 total intervals, the expected number of events per 2-day interval is 0.088 not 10. But even with this number we cannot assess the Poisson probability without knowing the rate of SIDS. Could you use the CDC rate of 34.7 per 100,000? Not really. We still need to know how many babies were vaccinated. There’s no way to impute that number without more information. The goal here is attempt to prove these incidents of SIDS within 2 days post vaccination are not random. Just looking at the numbers it certainly looks that way. But what if the number of babies vaccinated is huge compared to the number of SIDS cases within 48 hours of vaccination. If Steve can attain the number of vaccines administered during the 7 year period in question the rate of SIDS can be determined for this analysis.
- "How many babies? [...] the probability of a random babies chance of SIDS"?
We don't need to know that! Steve didn't determine the rate of SIDS. Instead, he made the simplified estimation that on the average each baby - also those 300 babies - got vaccine injections every 60 days, or 6 times per year ("like clock work", but that may be irrelevant). Any unrelated random event that occurs in a year will occur with a chance of ca. 1/360 on any day of that year; the chance for such event to fall inside any of the six 48h periods after vaccination is then 12/360=1/30. And 1/30 = 10/300. In other words, if the vaccines have no effect, we should expect that around 10 of those babies were injected within 48h before they suddenly died. Any strong deviation from that number is a so-called safety signal. And by the way: contrary to VAERS, reporting bias is not an issue here.
It's sad to see you reduced to the use of ad hominems Harry. Here's the problem.
What Steve is doing is hurting the entire effort to expose how dangerous many vaccines are. It's an enormous task with nearly 100 years of propaganda to overcome. The public has been brainwashed. With Steve pushing out flawed analysis after flawed analysis gives the opposition fodder with which to beat down the effort. And they will, I know these people. What we need is properly conducted, replicable research. Not hack jobs with data someone dropped on Steve's doorstep. That is not how science works.
Sorry Paul but it really looked as if you were trolling. I already explained that what you wrote didn't apply to the method that Steve used and I gave up. However, coincidentally it's similar to the one prosecutors used against Lucy Letby. The fault the prosecutors made was that they didn't include the other babies that died. That explanation here: https://twitter.com/profnfenton/status/1692837686166368660
To properly apply a Poisson distribution to model SIDS deaths following vaccinations one would need more than just the number of SIDS deaths and the vaccination schedule. Specifically, you would also need: The total number of babies vaccinated over the analysis time period. This is because the key Poisson parameter λ represents the average rate of SIDS deaths per vaccinated baby over a given time window following vaccination. Without knowing the total number vaccinated, you can't accurately calculate λ, the SIDS rate per vaccinated baby. Simply assuming λ based on the vaccination schedule alone is invalid. Without the full underlying dataset, any Poisson model will be based on unfounded assumptions. The stated 1/30 probability of SIDS within 48 hours of vaccination is purely hypothetical, not derived from actual data. The Poisson distribution models the probability of observing X events in an interval given a known average rate of events (λ). Without empirical data on the actual rate of SIDS occurrences within a time window of vaccinations, λ is unknown. Plugging in an assumed probability like 1/30 as λ gives an invalid model - since this doesn't represent the true observed rate. Any probabilities or conclusions derived from this assumed Poisson distribution are therefore statistically unfounded.
Okay, so let’s breakdown what Steve did here. He said:
“If SIDS is just randomly happening to babies, and babies are vaccinated every 60 days like clockwork, the chance of a SIDS death happening within any 48 hour window post-vaccine is 1/30.”
Hmm. Babies are vaccinated every 60 days like clockwork. How many babies? How can you assess the probability of a random babies chance of SIDS without knowing how many babies were vaccinated during any 60 day interval? You can’t. “Within any 48 hour window post- vaccine is 1/30th”. With this information you cannot determine what the rate of SIDS is. The rate of SIDS is = (the number of SIDS cases)/(total number vaccinated). It is not = (2 days)/(60 days). That is not a rate. It’s a dimensionless quantity. This is the problem. We only know how many SIDS cases there were spread out over 7 years and how many fell within a 2 day interval after vaccination. But it gets more convoluted because there aren’t 30 2 day intervals during a 60 day period as the vaccinations are made every day so there are 60 2 day periods with the last 2 day interval spanning days 60 to 61. But that does not matter. We are missing a crucial number here. We do not know what the rate of SIDS is because we do not know how many babies were vaccinated over that 60 day period.
Steve goes on to say:
“So if there are 300 babies who died of SIDS, we’d expect that 10 of them, on average, would happen within every 48 hour window post vaccine.”
If the 300 events and the 225 events that fall within the 2 day parameter are randomly distributed then we must spread them out over 7 years of overlapping 2 day intervals. Let’s work this out. There were 300 total events over 7 years 225 of those events occurred within a 2-day interval. Over 7 years there are 2556 possible 2-day intervals. If the 225 events are randomly distributed over the 2556 intervals we need each interval has an equal chance of containing 1 of the 225 events so to calculate the expected number per interval there were a total events = 225 and a total of intervals = 2556 so the expected number = Total events / Total intervals make the expected number = 225 / 2556 or the expected number = 0.088 events per interval. Therefore, if the 225 events that occurred in a 2-day interval are randomly distributed over the 2556 total intervals, the expected number of events per 2-day interval is 0.088 not 10. But even with this number we cannot assess the Poisson probability without knowing the rate of SIDS. Could you use the CDC rate of 34.7 per 100,000? Not really. We still need to know how many babies were vaccinated. There’s no way to impute that number without more information. The goal here is attempt to prove these incidents of SIDS within 2 days post vaccination are not random. Just looking at the numbers it certainly looks that way. But what if the number of babies vaccinated is huge compared to the number of SIDS cases within 48 hours of vaccination. If Steve can attain the number of vaccines administered during the 7 year period in question the rate of SIDS can be determined for this analysis.
Interesting. My check of your check:
- "How many babies? [...] the probability of a random babies chance of SIDS"?
We don't need to know that! Steve didn't determine the rate of SIDS. Instead, he made the simplified estimation that on the average each baby - also those 300 babies - got vaccine injections every 60 days, or 6 times per year ("like clock work", but that may be irrelevant). Any unrelated random event that occurs in a year will occur with a chance of ca. 1/360 on any day of that year; the chance for such event to fall inside any of the six 48h periods after vaccination is then 12/360=1/30. And 1/30 = 10/300. In other words, if the vaccines have no effect, we should expect that around 10 of those babies were injected within 48h before they suddenly died. Any strong deviation from that number is a so-called safety signal. And by the way: contrary to VAERS, reporting bias is not an issue here.
PS. Steve adjusted his numbers but the method remained the same.
Still wrong. These are not rates and do not apply.
Maybe "Don't feed the trolls" applies?
It's sad to see you reduced to the use of ad hominems Harry. Here's the problem.
What Steve is doing is hurting the entire effort to expose how dangerous many vaccines are. It's an enormous task with nearly 100 years of propaganda to overcome. The public has been brainwashed. With Steve pushing out flawed analysis after flawed analysis gives the opposition fodder with which to beat down the effort. And they will, I know these people. What we need is properly conducted, replicable research. Not hack jobs with data someone dropped on Steve's doorstep. That is not how science works.
Sorry Paul but it really looked as if you were trolling. I already explained that what you wrote didn't apply to the method that Steve used and I gave up. However, coincidentally it's similar to the one prosecutors used against Lucy Letby. The fault the prosecutors made was that they didn't include the other babies that died. That explanation here: https://twitter.com/profnfenton/status/1692837686166368660
To properly apply a Poisson distribution to model SIDS deaths following vaccinations one would need more than just the number of SIDS deaths and the vaccination schedule. Specifically, you would also need: The total number of babies vaccinated over the analysis time period. This is because the key Poisson parameter λ represents the average rate of SIDS deaths per vaccinated baby over a given time window following vaccination. Without knowing the total number vaccinated, you can't accurately calculate λ, the SIDS rate per vaccinated baby. Simply assuming λ based on the vaccination schedule alone is invalid. Without the full underlying dataset, any Poisson model will be based on unfounded assumptions. The stated 1/30 probability of SIDS within 48 hours of vaccination is purely hypothetical, not derived from actual data. The Poisson distribution models the probability of observing X events in an interval given a known average rate of events (λ). Without empirical data on the actual rate of SIDS occurrences within a time window of vaccinations, λ is unknown. Plugging in an assumed probability like 1/30 as λ gives an invalid model - since this doesn't represent the true observed rate. Any probabilities or conclusions derived from this assumed Poisson distribution are therefore statistically unfounded.
Good luck getting study funded!