A great example of this tactic is the following CDC “study” where they went through all 50 states looking for one where they could finesse the data to show that the covid vaccines reduced the risk of re-infection in people who already had covid before getting the vaccine. And whaddya know, they found one (out of 50 plus a few non-state jurisdictions like Washington, D.C.) where they could make the data say what they wanted it to say:

See, if the CDC was able to use more than one state to show that the covid vaccines reduced the risk of reinfection, they would’ve (duh). But they tried and tried until they found a state that they could torture the data to show this.

By the way, there is another important lesson for propagandists here: the value of persistence. Don’t just give up if you can’t find a dataset that’s easily doctored or manipulated to bolster a regime talking point. Sometimes you gotta get creative and keep at it until you strike paydirt.

V-2. Adjust Problematic Data

Yup, we mentioned this earlier in the section about rigging studies [see HERE].

If the raw data doesn’t conform to your preferred narrative, then simply “adjust” it until it fits, the same way you would for a study’s internal data. Data adjusting is a routine part of science, and since very few people actually understand how it works, you can take advantage of and abuse this practice.

Some fellow even published a Scientific article about the topic (it makes for interesting reading if you’re a geeky nerd):

A brilliant application of this concept relates to the Global Warming Scientific establishment consensus that used to be the Global Cooling Scientific establishment consensus. How do you think that the same data which showed in 1974 that the world was heading for an irreversible Ice Age that threatened humanity’s survival now shows that there was really a *warming* trend from the exact same datathat is threatening humanity’s survival??

They simply “adjusted” the data to make the earlier decades colder and the later decades warmer, and voila, problem solved! It’s devilishly cunning and highly effective – observe in the chart below (from a noted regime dissident heretic) the two lines that track the average annual temperature: blue line = the raw data and the orange line = the data after the regime scientists “adjusted” it:

If you look at the blue line, there’s no overall warming over the past 100 years – which is very bad for the official narrative of CATASTROPHIC GLOBAL WARMING!!! However, the orange line shows a clear warming trend over the past 100 years – which is exactly the narrative.

Of course, if in the future for whatever reason it becomes pragmatic to revert back to Global Cooling, then the regime scientists over at NOAA will simply “re-adjust” the data to make the past 100 years look like a steady cooling trend.

Point is, it’s all in the adjustments.

(Note: It’s useful to allow a few random low-profile regime science heretics to hang around because they produce data and analysis that is actually quite helpful for the regime’s own internal use, as long as you make sure they don’t start gaining prominence – then you cart them off to Guantanamo Bay without delay.)

V-3. Exclude from Official Analyses of Official Data Anything That Doesn’t Fit with Your Desired Results

Carefully vetting what gets included in your analysis is literally 101 stuff. If information or actual results threaten to undermine your preferred results, just exclude them from official analyses of the official data. So, if there’s a government database that shows that, after the Glorious Vaccine the incidence of a bunch of medical conditions went up a lot, just ignore it.

Take the VAERS (Vaccine Adverse Event Reporting System) database jointly managed by the CDC and FDA.

The CDC (pretends to) encourages reporting to VAERS medical conditions that manifest after someone gets vaccinated, “even if you are not sure that the vaccine caused the illness.”

After the covid vaccines were rolled out in mid-December 2020, the VAERS entries for deaths look like this (chart shows the total number of reported deaths for all vaccines each year):

This graphic shows stats for VAERS reports of injuries or deaths from the covid vaccines:

Yet, when was the last time you heard about VAERS from the CDC in any statement or analysis concerning the precious covid vaccines?  Exactly!! The CDC (and everyone else) simply ignores VAERS (except when they from time to time issue “fact-checking” pieces to debunk VAERS).

Also, make sure to relentlessly hound into oblivion anyone who dares to try and use such data to undermine the credibility of your regime analyses and proclamations. This is often a problem because inevitably there will be a bunch of people who have access to the raw data once it exists.

V-4. Piggyback on Prior Established Relationships and Differences

An easy way to jury-rig a study is to compare two entities that you know already have a particular difference or correlation. You can then pretend to “discover” this difference or correlation but attribute it to a new factor.

So, if for instance since poor states compared to rich states tend to have worse health outcomes, if the poor states happen to be less compliant with regime guidance, you can point to their worse health outcomes and blame it on them not taking the Glorious Vaccine. The media really excels at amplifying this message in particular, because they love nothing more than attributing bad outcomes to political affiliation with the “bad” political party or parties.

V-5. Control Critical Datasets Used for Scientific Research

He who controls the data controls The Science.

Take care to have ironclad control over the most prominent and widely used datasets and you will save yourself much stress and headaches. For instance, the military controls their internal datasets and can manipulate them at will. Like DMED – they doctored this dataset all right to the point of rendering the whole thing useless. Take a look below at the following two charts showing the *same* DMED data for “rates of ambulatory doctor visits” for the years 2015-2018.  The left chart is the version published in 2019 and the right chart shows the 2021 version – and somehow, they are not the same (red circled areas).

Notice the change in the 2016-2018 numbers (which you can see by the shape of the trend line)? How did the number of doctor visits that occurred in 2016 increase between 2019 and 2021????  Because the regime simply rewrote the data. That’s what you can do when you have complete control over the dataset.

It goes without saying that under no circumstance should you allow any heathen scientists access to the sacred texts or data of Science under your control – remember, you must be always vigilant lest a rogue heretical researcher perform an analysis that could invalidate or contradict The Science. The CDC leads by example here:

If you don’t give annoying pesky independent scientists access to the data, you don’t have to worry about them discovering things in the data that will undermine the regime narrative big-time.

About the Author

Aaron Hertzberg is a writer on all aspects of the pandemic response. You can find more of his writing at his Substack: ‘Resisting the Intellectual Illiteratti’.

Featured image is taken from the front Cover of ‘The Complete Idiot’s Guide to Cooking Data for Aspiring Propagandists’.