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How Scientists Use Statistical Deception to Fake Influenza Vaccine Effectiveness

Flu shot giftNote: As we approach the dreaded "FLU SEASON" which is now like the season of Rudolph the Red Nosed Reindeer in my house, 10 months a year, we are pleased to share this article from VacTruth.

By Tom Stavola

Statistical manipulation misinforms people by use of false measurements.

Vaccine scientists often conceal the true effectiveness of the influenza vaccine through risk calculations. Researchers use a calculation that essentially artificially inflates the effectiveness of influenza vaccines. Rather than use the statistical measure that more truthfully represents vaccine effectiveness, the researchers choose to use a statistical measure that makes vaccines appear more effective than they truly are.

Why is this important?

The published studies that report high effectiveness rates are then used by governmental agencies, your pediatrician, and mainstream media to attempt to increase influenza vaccine uptake rates. In other words – it is a tactic designed to convince you to get the flu vaccine every year.

To understand this specific deception technique, you will need to understand two risk concepts used to obscure findings: Relative Risk (RR) and Absolute Risk Reduction (ARR).

Relative Risk (RR) Explained

Relative Risk compares the chance of a bad outcome between two groups. Statistical definition: the proportion of bad outcomes in the experimental group (group #1) divided by the proportion of bad outcomes in the control group (group #2). Relative Risks under 1.0 indicate the tested medical intervention helped patients, while relative risks over 1.0 indicate the medical intervention hurt patients.

Absolute Risk Reduction (ARR) Explained

Absolute Risk Reduction measures the absolute difference in bad outcomes between two groups (group #1 and group #2). Statistical definition: the proportion of bad outcomes in the control group minus the proportion of bad outcomes in the experimental group (in this case, a larger number means the treatment was helpful, while a small number means the treatment didn’t do much to help, and a negative number means the treatment was hurtful).
Why RR and ARR are Different

Relative risk tells you how badly one group fared in comparison to another group, in relative terms. It’s akin to saying, “Johnny jumped half as high as Timmy” – but you never know how high each of them jumped. Absolute risk reduction – on the other hand – tells you the actual difference in outcomes between the two groups. This would be akin to saying, “Johnny jumped 8 feet high, but Timmy jumped 16 feet high.” One can see that the absolute risk reduction is a more helpful, informative statistical measure; whereas, it’s easier to hide the magnitude of the differences behind relative comparisons.

Again, why is this relevant to vaccines?

The general public is interested in one question: if I choose to get the influenza vaccine, will my chances of getting the flu decrease, and by how much? We are not interested in relative comparisons, we are interested in actual, true differences.

Let’s break down a practical example into 4 easy steps to demonstrate the disparity between relative risk (RR) and absolute risk (AR).

Step 1: Define the Two Groups

Good scientific practice is to separate people into groups as closely numbered as possible, and as well matched as possible, meaning, we attempt to compare groups of people in which all variables are the same (or very similar) except the one variable we’re testing. This way, we can tell that any significant difference between the two groups is due to the variable we’re testing, and not some other variable.

One of the groups will receive the treatment (let’s say vaccine), and another group will receive nothing. This is called the control group. Typically, gold standard medicine would dictate using a saline placebo on the control group (a placebo is a harmless substance that has no effect – used to make the control group think they’re getting the treatment).

In most vaccine experiments, researchers incorrectly use another vaccine or a vaccine ingredient as the placebo. This way, the differences between the two groups will be reduced (muted), and the researchers can report in their conclusions, something to the tune of, “vaccines didn’t increase the amount of adverse reactions significantly.”

So – back to our example.

Let’s assign 25 people to the control group (these people receive the “placebo” that is actually another vaccine or a vaccine ingredient), and 25 people to the vaccine exposed group (these will be the people who get the vaccine).  Read the rest of this article and take a good look at the graphics here.




Talk about Providential!

Just within the last few hours I wrote a piece on this very issue (, which included a brief video ( of Professor Gerd Gigerenzer of the Max Planck Institute for Human Development in Berlin explaining it so much better than I could.

david m burd

Thankyou Tom Stavola,

Here is my Post on AoA several years ago, relevant to your Post; please check it out; and then I'll add a few thoughts:

What Flutracking in Australia DID NOT do was to record any of the myriad "side effects" of the flu vaccine as they were not motivated to do so. As Flutracking in Australia has shown for many years there is basically a meaningless difference in "work days lost" and/or severity of any flu-like symptoms between those taking/not-taking flu shots.

HOWEVER, in Australia their flu shots do not have Thimerosal, whereas here in the U.S. most all injected flu shots indeed have, and always have had, and still have 25 ugm (micrograms) of ethylmercury. This is proven by all the U.S. Flu Manufacturers themselves as amply proven every year by their detailed flu-vaccine production data (of course the CDC cites otherwise but they are
not being at all honest --- imagine that!

If you want please contact me at:

David M Burd

Jeannette Bishop

Thank you! This post clearly illustrates the two representations of risk.

Sophie Scholl

Everyone needs to know and understand the Relative Risk scam !

August now, they will start banging on about their 60 percents in September I suppose .


These types of studies are rarely if ever double blinded, which creates an even more fundamental issue. Doctors, due to their heavily conditioned belief in vaccine effectiveness, will necessarily bias their diagnosis based on patient vaccine status. This is why double blinding exists. This will inherently skew cases toward the unvaccinated group, creating a self-fulfilling appearance of effectiveness.

In the case of measles, e.g., the CDC explicitly encourages this bias ( "To minimize the problem of false positive laboratory results, it is important to restrict case investigation and laboratory tests to patients most likely to have measles (i.e., those who meet the clinical case definition, especially if they have risk factors for measles, such as being unvaccinated, recent history of travel abroad, without an alternate explanation for symptoms, for example epi-linked to known parvovirus case) or those with fever and generalized maculopapular rash with strong suspicion of measles."

They are less explicit about encouraging this bias with respect to influenza, but without blinding it cannot be ruled out.


Got ahead of myself-- How are the "95% CI for difference" values established?


Anybody care to explain how in " Test and CI for Two Proportions" where the numbers

Gary Ogden

Relative risk (RR) is widely used in industry-funded drug trials because it vastly increases the appearance of efficacy, thus greases the skids for approval, public acceptance, and profits. A famous example is statins (those of us who have paid attention to the heart disease issue know that blaming cholesterol for heart disease is like blaming the firemen for the fire). The absolute risk (AR) of heart attack for men above 60 is roughly 3% per year. The best statin trials reduce that to 2%, for an AR reduction of 1%. But the media (who are a marketing arm of pharma, as we well know) trumpet this as a 33% reduction (1 divide by 3, times 100), using RR. What they don't say is that the increase in other conditions caused by statins makes them of negative value. For example, statin use increases the risk of Lou Gehrig's disease (ALS or MND) 50-fold, to that 1% who didn't have a heart attack; additionally so many simply discontinue them due to muscle damage. Yet these phony drugs are among the top pharma money makers.

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