Attempts to discredit the New Zealand data fall short. Way short. Anyone want to bet me I got it wrong?

A bunch of people are making attempts to discredit the NZ data leaked by Barry Young who is now facing 7 years in prison for his actions.

All of the “analyses” claiming “there is nothing to see here” are flawed, but I’ll let you decide that for yourself.

I’m going to start with the “analysis” just published by OPENVAET and another he co-authored with DR AH KAHN SYED. I’ll add to the list as more are published.

I assure you the NZ data is real and all attempts to discredit the data and what it says will backfire on those who attempt to do so as I will demonstrate in this post.

I’m offering to bet anyone $250K or more that the NZ data shows the vaccines are unsafe. I’ll be thrilled if I get any takers.

But this shows you that none of the people who boldly claim I got it wrong have any confidence in their “analyses.”

CLAIM: “The “New-Zealand whistle-blower” story is a dead-end for valid arguments”

The article claims include:

1. “Too many people are falling for the New Zealand Data trap. There are no alert signal (sic) in the New Zealand mortality trends and the data released is unusable.”
2. “Alterations made to the data by Kirsch are forbidding serious re-analysis”
3. “The data made public has been “obfuscated”. In layman terms, this means it has become impossible to verify, and useless for any form of real analysis”
4. For all 8 ten year age groups listed in the article, 20 and older, as well as for all age groups combined, the per capita deaths in 2022 were higher than 2020.
5. “Furthermore, Kirsch is now undermining Andrew Bridgen’s efforts in the United Kingdom”

Wow. This is an evidence free post. Let me respond to each point.

1. There are huge alert signals if you have spent time with the data. The definitive analysis method for an intervention like this is the time-series cohort analysis. Yet OpenVAET doesn’t even mention they looked at it. That’s just ridiculous. He simply does a population analysis and finds that deaths are up for all 8 ten year age groups compared to 2020, so he says nothing is happening in New Zealand. Here is the mortality by week in New Zealand. Does it look like nothing is happening here?

2. He doesn’t explain what alterations we made, so how can he know the data is unusable? We ran the time series analysis on the original data and on the obfuscated data and the results were an EXACT match. And that’s the definitive way to analyze this data. So I’d like to see his EVIDENCE that the data is unusable for analysis. He provides NONE whatsoever.
3. See #2.
4. The data he shows shows mortality increased in every 10 year age group from 20 years on up. So how is that a nothing burger? Had they looked at the time series cohort analysis they would have found huge signals, but they decided not to look.
5. For proof of #5, they cite this tweet with 153K views. This was Andrew’s idea. Read the comments. Does this sound like I’m undermining his efforts? I was a major funder of the whole event in Parliament.

If you are going to criticize someone’s work, the least you can do is look at it first. If you look at the graph I posted above which is publicly available data (if you know where to look), it’s pretty clear something is wrong and it is crystal clear in the time series data which they NEVER looked at. Something is causing record level peaks.

Claim: “The New Zealand “whistleblower” data is a burger of nothing.”

This new article is co-authored by Dr. Ah Khan Syed.

The key claims include:

1. This is the definitive takedown. There is nothing to see here. The debate is over.
2. “The rise in mortality in NZ appears to be explained by the increase in the elderly component of the overall population. That in itself is somewhat bizarre but not a subject for today’s analysis.”
3. “This curve shows nothing but a slight increase explained by the aging of the cohort.”
4. What you can see is that there are actually less deaths in the cohort than should be expected based on the background data – about 14% less
5. The data was deliberately “released as bait” and it under-reports deaths:

Wow. Once again, they ignore the accepted definitive way to analyze the data (time series cohort analysis) and they “roll their own” analysis method and model. They cannot be questioned on this since they are the experts and they don’t have real names. So let’s tackle their key points:

1. Anyone who claims their analysis is the final word shouldn’t be trusted. Science is all about questioning and being open to be questioned. These “scientists” are claiming they got it right and the case is closed. The NZ data has lots of signals. Just because these two people are incapable of finding the signal is not proof that the signal isn’t there. And the fact that they never look at the time series data and claim no signal is preposterous. They’ve both destroyed their credibility here.
2. Wow. Maybe you should get to the bottom of the mortality rise issue before declaring the data Barry released don’t show anything?
3. The curve they show is too confounded to show anything. And then they give a hand-waving “This curve shows nothing but a slight increase explained by the aging of the cohort.” Really? Where is the evidence behind that statement?
4. There are less deaths in the cohorts when they first get the shot. It’s called HVE and there are two types. I talk extensively about this in my upcoming article on the NZ data. They are completely unaware of the effect which tells you that they are newbies with respect to analyzing vaccine safety data.
5. Where is the evidence this was “bait?” AFAIK, there has NEVER in human history been a case where a health authority released manipulated data in the guise of a data breach. Any health authority that pulled such a stunt would destroy any remaining credibility that they had. There is no such thing in human history AFAIK. The authors do not cite a precedent. I’ve spent hours talking to Barry and nearly 2 months analyzing the data and I am constantly amazed how it passes all the statistical “tests” I throw at it. I analyzed it in ways nobody would have thought of and the data is well behaved with no anomalies. If the data is bait, where is the proof in the data or in an admission? These people are simply making this stuff up out of thin air.

Igor Chudov’s analysis

Igor had the good sense to re-think his earlier comments. See this post.

The Barry Young analysis by lot number and the M.O.A.R. analysis

I have been focused on the big picture (the time series cohort analysis).

I have not had a chance to look at the lot number analysis in detail. There are 124 lots to look at.

Here’s an example of a huge safety signal in Lot #10:

This pattern is impossible for a safe vaccine.

Yet none of the “experts” will be able to tell you why! I’ll reveal why in my upcoming article. Everyone who claims to be able to analyze data should be talking about this!

Here’s another example to show this wasn’t a fluke:

And here’s a third example that is even more stunning than the previous 2 charts:

Again, the “experts” have absolutely no clue why these charts are so stunning. That’s one of many reasons they say this data is a nothing burger.

What they are really saying to you is “I don’t know how to analyze this data, but I’m going to attempt to convince you that I know what I’m doing and that Steve Kirsch doesn’t.”

History has shown that is a losing proposition.

But just to make this crystal clear to everyone….

My offer to anyone who think there is not a serious safety signal in the leaked NZ data or that the data was gamed or unusable

I’m willing to bet $250K or more that the data is legit and shows a serious safety signal. Anyone want to take my bet? Same terms as my bet with Saar Wilf (neutral panel of expert epidemiologist judges picked by a mutually agreeable consulting firm who vote secretly).

I set the bar at $250K but I’m willing to go to $10M on this one.

I predict crickets.

People who claim I’m wrong and who won’t accept my bet are basically telling you that they have no confidence whatsoever in their analysis.

In Texas, they have a saying for that: “Big hat, no cattle.”

Money is a great way to make that clear to people.

What do you think?

SummaryI will update this article as more “analyses” come out.

My advice in the meantime:

• If they aren’t doing a careful analysis of the time series cohort data, stop reading.
• If they claim the data shows the vaccine is safe or is reducing all-cause mortality, stop reading.
• If they claim that the data is insufficient, missing data, systematically biased in a way that makes it unusable, manipulated, false, or that you need a control comparison group (i.e., data that is not publicly available), stop reading.
• If they aren’t accepting my $250K or more bet, they are basically telling you they aren’t really sure whether they got it right or not. Otherwise, why not take my money?

I’ll be coming out with my own extensive analysis of the NZ data shortly which will make all these points crystal clear. In the meantime, the lack of any takers of my offer should be a pretty good clue as to who got it right.

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