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 ╬╗ 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 ╬╗, the SIDS rate per vaccinated baby. Simply assuming ╬╗ 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 (╬╗). Without empirical data on the actual rate of SIDS occurrences within a time window of vaccinations, ╬╗ is unknown. Plugging in an assumed probability like 1/30 as ╬╗ 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.
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
PS And here's the method that Steve applied: https://twitter.com/Fuengi/status/1687498902079954944
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 ╬╗ 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 ╬╗, the SIDS rate per vaccinated baby. Simply assuming ╬╗ 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 (╬╗). Without empirical data on the actual rate of SIDS occurrences within a time window of vaccinations, ╬╗ is unknown. Plugging in an assumed probability like 1/30 as ╬╗ 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!