Yet Another Flawed “Fact Check” on the NZ Data
By STEVE KIRSCH
Executive summary
Another day, another flawed fact check.
This one comes with a bonus about Professor Jeffrey Morris. Morris told the reporter that it is OK to download and analyze the New Zealand data, but not OK to talk about it.
I thought I had heard it all, but that’s a new one!
OK, so if no honest scientist is allowed to talk about this data, then how the heck can the fact checker say my analysis is flawed??
A head scratcher for sure!!!
So let’s refute the fact check first, then we’ll have some fun with Professor Morris for dessert.
The claims of the fact check
The fact check claims that the reason mortality rises after the shot is because the shots were given when deaths were at a deficit and so death rates were rising.
This is complete bullshit. What are these people smoking??? I wish I knew!
Deaths fall every August like clockwork in New Zealand:
So I looked at people who got the shot in August, 2021:
Deaths since injection date in August 2021. x-axis is the number of days since the shot. y-axis is the number of deaths in the time period. Borders are closed, no COVID and any temporal HVE effect that might exist is gone after the first bar here (the first month).
The death rate climbed 43% when it should have gone down by 22%.
Don’t need a calculator on that one.
I’ve heard people try to claim “temporal HVE” or it was COVID deaths or it was because the vaccinated the “frail and elderly first.” There was no COVID in this period, the borders were closed, and temporal HVE never lasts over 21 days (and this data shows it was gone after 2 weeks because New Zealand basically tried to vaccinate everyone who was still living because the “about to die” were viewed as a threat to the living). And the “frail and elderly” is completely bogus because these people die just like everyone else: any fixed group of people of any age will die at a progressively smaller rate over time (if nothing is going on in the background).
So they are grasping at straws. It shows how desperate they are to propose explanations that simply do not fit and have no evidentiary basis.
So this “fact check” relies on a hand-waving argument with no evidentiary support. Are you surprised? These people never bother to check what they are told. They just eat it up hook, line, and sinker.
So now you know why I can’t find anyone qualified to analyze data of this type to challenge me one-on-one on the data: this data is DEVASTATING. That was just one small example.
Who is qualified to analyze this data?
Apparently very few people know how to do this right.
I see very lame attempts to analyze this data from lots of people.
There’s a common thread: each person rolls their own method!!!! Have you noticed? Each says he’s the expert, yet each has a different method.
WTF!?!? Come on. There are known right ways to analyze this data including:
- cohort time-series
- plotting time between each shot and death (age stratifying for extra credit)
- doing triangle plots of all the deaths using numbers (not dots)
A lot of people throw these proven methods to the wind. Or they execute them carelessly like William Briggs did with his triangle plots.
Only a few get them right and can crunch the data and interpret the results correctly. But these analyses aren’t out yet because they take time because the people doing them are careful.
One of the reasons this is hard is that nobody has ever seen data like this before. It’s always kept hidden. And the people with access to the data as part of their day job are told not to find any safety signals. So it is a lost art apparently.
Anyone who claims they are an expert in data analysis tells you you can’t analyze this data and find a signal is either a fool or is gaslighting you.
To date, I haven’t been impressed yet with any of the analyses that have been published by third parties, but Clare Craig is coming out with an analysis soon that I think I’ll like.
And now for dessert…
The Professor Jeffrey Morris story: did he lie to the press or was he misquoted?
Before going public with the New Zealand data on November 30, I shared the data with Professor Jeffrey Morris to see if he could refute my analysis. As a professional misinformation superspreader with millions of readers worldwide, I can’t afford to make mistakes, especially on something this big. So I wanted to see how I would be attacked so I could proactively defend my work.
Morris downloaded the data and then wrote R code to duplicate my cohort time-series analysis so he could compare his results with mine.
But the problem is that the fact check reporter seems to think that Morris didn’t download the data because it was tainted!! Here’s the excerpt:
When I read that, I contacted Morris and asked him if he lied to the reporter.
He said he didn’t. He wrote to me in a DM, ““Her wording does not accurately reflect what I said and I have asked her to correct it.”
Wow. That’s a huge misinterpretation for a fact check reporter.
Morris was able to IMMEDIATELY get the story corrected, a remarkable feat that I have never been able to accomplish with any “fact checker.”
Here’s the new text:
This is still misrepresenting the facts. Morris downloaded and analyzed the data.
You can’t rewrite history.
The proof he downloaded the New Zealand data
“Hey, I just met you… and this is crazy… but here’s my number… So call me, maybe?”
I’ve been hoping to find a Carly Rae Jepsen fan over there, but so far, no such luck. Call me, maybe?
Summary
Another fact checker bites the dust. I really need to speak to the Health New Zealand epidemiologist if we are going to resolve this.
I have asked Health New Zealand many times to either publish their epidemiologists’ analysis of the vaccine data or allow me to chat with them so they can tell me how I got it wrong.
The response is always silence.
I wonder what they are afraid of? The truth perhaps?
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