Last year, Aaron Hertzberg compiled an idiot’s guide on how to convince the masses that there is a deadly pandemic, when there isn’t one, and pretend there are no injuries caused by the vaccine, when there are.
He has written the text for aspiring propagandists who would like to learn the art: “For the beginner, [the art of propaganda] can be very difficult to master. Even the experienced propagandist can at times fall into the trap of thinking that creating and disseminating propaganda is a straightforward enterprise – which is a good way to win a permanent all-expenses paid Siberian vacation,” he said.
“The following short guidebook will provide the aspiring propagandist, WEF lackey, Communist Apparatchik, Woke Marxist and seasoned government bureaucrat alike with the tools and knowledge necessary to develop their promising talent into full-bloom mastery of the art of propaganda.”
As one can imagine, Herzberg’s guide is necessarily long. We are publishing one section at a time so hopeful propagandists don’t feel overwhelmed and give up on their dreams of a career in propaganda after the first hurdle.
– RHODA WILSON
By Aaron Hertzberg as published by the Brownstone Institute on 20 December 2024. The article was originally published on Hertzberg’s Substack page on 15 June 2023. For the introduction, which includes links to all sections, and ‘Section I – Definitions’ read HERE.
Perhaps the most crucial skill set necessary to manipulate Science is the ability to design and manipulate a study to achieve the necessary results.
(Note: the actual rigging of studies will always be done by experts who run studies for a living (called PIs or Principal Investigators). So, you don’t really need to be fluent in this stuff. But it is nevertheless helpful to have a decent enough grasp of the basics.)
Studies – especially the big fancy ones that are typically considered to be the “gold standard” of Science™️ – are massively complex beasts that can be manipulated in innumerable ways. We will explain the more prominent and straightforward types of deceptions, manipulations and design flaws that can be exploited to make the study a puppet in your hands to jerk around at will.
(Note: There are many gradations of sophistication in implementing any of the following manipulations. We’re only going to explain and illustrate the underlying concepts using the straightforward plain application of the principles, without adding any fancy adornments and baubles. The goal here is that you should understand the various types and ways to manipulate data. You can educate yourself in the more advanced methodologies afterwards, something which is of course highly encouraged and recommended.)
Table of Contents
IV-1. Study Rigging Tactic #1: Rig the Design of Study Protocols
Most of the material relevant to this section is also relevant to the next section dealing with sabotaging the implementation of the study protocols, so we will only address here the tactics unique to rigging the design of the protocols themselves.
Study protocols are basically like a rulebook dictating how the study is going to be done. So, make sure to write rules that favour the outcome you need to get.
A) Stacking the Deck; Strategically Assign Study Subjects to the Respective Study and Control Groups
Almost all big, special studies have two groups: the study group and the control group. In a study for a new medicine, the study group gets the medicine and the control group does not. In theory, if the medicine works, then there should be more sick people in the control group than in the study group.
So, if you are running a study to test a new regime Wunder-drug, you could exploit this by putting more unhealthy people in the control group than in the study group so that the study group will do better even if the regime drug doesn’t work. (You should of course not admit to doing this or any other of these tactical shenanigans in study documentation.)
B) Carefully Vet Subjects To Be Included In The Study
Much headache can be avoided simply by keeping out people who are likely to mess up your results in some way.
For example, if you’re testing a novel drug that you want to prove is safe and effective, keep out people who are particularly disposed to suffer bad reactions or inefficacy. You get the idea. (Like they didn’t include any old co-morbid people in the covid vaccine trials, which would have exposed the “99% effective” canard.)
IV-2. Study Rigging Tactic #2: Sabotage the Execution of the Study Protocols
Often enough, you will not be able to rig the study protocols themselves outright to produce your desired results. In such cases, you need to sabotage the implementation or adherence to the official study protocols instead. This is quite easy to do, and there are literally endless ways to accomplish this.
(Note: It is prudent to have your logistics planned out in advance, to avoid a variety of problems and stressful situations that can pop up in a big study involving thousands of subjects and personnel. For instance, if you want to “show” that a particularly annoying drug is actually lethal, you should have body bags on hand to quickly remove bodies from public locations and a cremation facility on call 24-7 to destroy any undesirable forensic or pathological evidence that corpses may contain.)
Protocol Sabotage #1: Administration of the Study Treatment or Intervention (To The Study Group)
People think that giving study subjects a drug is uncomplicated and straightforward. They’re wrong. Very very wrong. You can frequently control the entire study by subtly adjusting how the treatment gets administered to the study subjects, including the following:
– Dosing/Quantity of intervention – You can underdose or overdose a drug depending on what you’re aiming for. If you want the drug to look ineffective, underdosing will ensure that it won’t work. If you want to show that the drug is dangerous, just amp up the dose to highly toxic levels.
– Timing of treatment administration – Another way to sabotage a drug is to give it to patients too early or too late to be effective. There are many different tacks you can choose to accomplish this. For example, you can send the drug to patients through the mail, which will inevitably add a few days to the timetable (a David Boulware Ivermectin special).
– Quality of the product, i.e. purity or potency – A contaminated or poorly manufactured product will not function the same way a pure product manufactured with high-quality ingredients and complete fidelity to the ideal manufacturing practices.
(Note: You should ALWAYS conduct off-the-record pre-clinical studies on animals – and humans – to understand how different versions of the drug or intervention will function BEFORE deploying contaminated versions in a study (in addition to the official pre-clinical studies on the normal formulation of the drug); otherwise, you run the risk of accidentally sabotaging your own sabotage attempts. Remember, the point of running the study is to show a pre-ordained outcome, not discover any novel scientific insights! Uncertainty or unpredictability about what the drug or intervention you’re studying will do in real life is Kryptonite to successful study rigging. Or at minimum is going to give you some really bad migraines while you struggle to navigate the maze of hazards and uncomfortable data from your now-extremely messy study.)
– Use saline or placebo instead of the intervention – Another way you can minimise hazards of the regime’s chosen intervention is to give a placebo instead of the treatment so that there is less exposure to the toxicity of the intervention. Obviously, you need to also make sure that using saline won’t have the unwanted side effect of showing that your drug doesn’t work, so this tactic is typically used in conjunction with other protocol manipulations or infidelities.
– Mix and match – You can always mix and match within any of these suggestions. For example, you can give some of the treatment subjects a different product. You can also employ more than one of these suggestions in combination so that you cover different portions of the study group with different suggestions, which can make it harder for outsiders to discover the protocol violations.
Protocol Sabotage #2: Administration of the Placebo (To The Study Group)
This is essentially the flip side of the previous section. There are a few specific tactics that are a bit unique as applied to the placebo:
– Give the control or placebo group the intervention – One way to guarantee that a study won’t show any efficacy for a treatment is to give the control group the treatment as well. If both groups get the treatment, then there won’t be a difference between them showing that the treatment group fared better because of the treatment.
The easier but riskier method of doing this is to have the study personnel directly give the drug to the control group masquerading as the placebo. (This is easy enough because the placebo is supposed to look, feel, taste and smell identical to the treatment to prevent the control group subjects from figuring out that they didn’t get the drug.)
The more difficult but less risky method is to nudge the control group subjects to obtain the treatment outside of the study. For instance, you can use a placebo that is markedly different from the drug. Since the study subjects can easily discover via Google that this isn’t what the drug is supposed to look, smell or taste like, they will endeavour to procure the actual drug on the side since they don’t want to die or suffer debilitating complications from whatever disease or condition the drug is being used to treat.
Alternatively, you can choose to run the study in a place where the population already has wide exposure to the treatment being studied, so the pool of subjects will be thoroughly contaminated with people already using or at least have a supply of the drug on hand.
(Just keep in mind that this tactic runs the risk of being noticed by pesky dissident anti-Science heretics since it will be a matter of public record that there was widespread awareness and/or use of the drug where the study was conducted.)
– Spike the placebo – If you don’t want an inert placebo, you can spike it with something a bit more “lively” that can elicit side effects and/or a therapeutic effect.
One specific method is to use components of the treatment to spike the placebo. This can be especially useful for hiding problematic side effects of a treatment that are caused by other ingredients or components besides the active treatment ingredient – if you put those in the placebo, then both groups will have similar side effects.
(Note: Keep in mind that if the side effects are too pronounced, simply putting the toxic components of the treatment in the placebo may raise questions if people notice that the rates of the specific side effects are vastly higher in the study’s control group than they are in the general population.)
Protocol Sabotage #3: Incentivise the Study Subjects to Modify Their Behaviour
The behaviour of study subjects is often a critical consideration when designing protocols and running a study. Use this to your advantage.
There are 3 basic types of incentives:
– Financial inducements – One of the surest ways to incentivise behaviour is to reward it financially:
- You can run a corrupt bribery scheme within the study. For example, if the study is obtaining results by asking subjects to report information – such as what side effects they experienced after getting the Glorious Intervention – you can pay subjects to not report side effects. However, you also will have to enforce secrecy and ensure that nobody finds out about it, which can be tricky.
- Alternatively, you can manipulate or take advantage of the environment where the study is taking place to function as your intermediary or middleman to dispense the financial goodies. For example, if you are testing the effectiveness of a potential intervention to block transmission of the Dreaded Disease, you can run the study in a place where people can only go to work if they are not infected with the Dreaded Disease, taking advantage of this built-in incentive to not report testing positive that people have (they want their full paycheque).
– Social Pressure – The second type of incentive is social pressure. This can come from peers, political forces, social groups, professional associates, institutions, celebrities or any other source of influence in society. The point is you can use any or all of these to your advantage.
For example, let’s say that you are running a study to test the effectiveness of the Wondrous Cloth Shield that stops the spread of the Dreaded Disease. So, you give some villages in a third-world country the Wondrous Cloth Shield and create a control group of villagers who don’t get the Wondrous Cloth Shield. You can make a show of how amazing these devices are in front of the villagers who get them. You can also have the village elders proclaim that the Wondrous Cloth Shield is a Gift from Heaven, which makes it a point of moral virtue to wear one, and more importantly, makes wearing one but getting infected with the Dreaded Disease a mark of religious failure. This makes them far less likely to report cases of the Dreaded Disease, especially compared to the villagers who weren’t given the Wondrous Cloth Shields. Which makes it look like the Wondrous Cloth Shield works to reduce Dreaded Disease transmission.
– Harsh Penalties – You can threaten all sorts of dreadful consequences if study subjects don’t do exactly what you want. This is especially easy to implement in third world countries where there is little if any rule of law and corruption is the rule. It might be useful to make an example of someone in advance to show that you mean business – for instance, you can pick someone at random to ship off to a prison in Sudan, from which they are unlikely to ever return alive.
Protocol Sabotage #4: Hire Incompetent People To Run The Study
Studies – especially the studies that perform some sort of experiment (as opposed to just analysing pre-existing datasets) – typically require a large staff to conduct. Hiring incompetent staff is a great way to give yourself some leeway to “massage” inconvenient data that emerges from the study – “this data is erroneous because the staff messed it up.” So of course, you have to “fix” the “errors.”
More importantly, incompetent staff are less likely to notice that you’re rigging the study because they don’t have the knowledge or experience about how a legitimate study is supposed to be run.
Protocol Sabotage #5: Remove Any Problematic Study Subjects or Events From The Study
This one is an obvious “Duh.” If a few subjects in the Phase 3 trial for the Glorious Vaccine suffer severe injuries right after getting injected with the Glorious Vaccine, well, you can’t have them ruining the “safe and effective” narrative. But thankfully, the solution is simple: remove them from the study.
This won’t even look suspicious to an outside observer! Every study has rules written into the protocols that allow you to kick out subjects who violate the study protocols or wish to leave for “personal reasons.” (Think of every time a politician says he’s resigning to “spend more time with his family” – same idea.) But most academics are suckers for this and fall for it every time.
If you’re really smart about how you design the protocols in the first place, you will add a condition that prohibits subjects from seeking medical care from any doctor outside the study. So, if a subject suffers a nasty side effect, like a bit of safe and effective myocarditis or some mild Bell’s palsy that leaves him somewhat paralysed, they’re gonna go straight to the nearest ER … which is a clear violation of the study protocols!! Bye-bye problem.
If you want to see a real-world maestro, look no further than the fellow in charge of Pfizer’s Phase 3 Kiddie trial for their vaccine – when one of the trial subjects by the name of Maddie de Garay suffered multiple rather nasty neurological injuries 24 hours after getting the vaccine (the sort that involves permanent use of feeding tubes and wheelchairs among other lifestyle “adjustments”), they simply threw her out of the study. And then wrote up her injury as “unresolved abdominal pain.” They also threw out another fellow from the main trial, a lawyer named Augusto Rioux, after he got some mild safe and effective pericarditis following Dose #1.
Same for AstraZeneca. Brianne Dressen was chucked following Dose #1 but they reported that she withdrew for personal reasons. See? Easy-peasy.
Protocol Sabotage #6: Record False Data
When all else fails, you can simply record data for the study that is dead wrong and fabricated out of thin air. Pfizer study contractor Ventavia shows us the way on this one. The following screenshots are the actual email sent by Brooke Jackson, one of Ventavia’s Site Managers, who decided to attempt to undercut the regime by exposing the ongoing fraud:
In an unusually prompt and effective response, Mrs. Jackson was fired less than six – 6 – hours after sending the FDA this email. SIX HOURS!! That is how things should get done.
Furthermore, when she sued in Federal court in an attempt to bring down the entire Pfizer vaccine trial, the regime successfully stalled it for almost two whole years using a variety of ingenious legal tactics. (However, it should be noted that whoever was in charge of hiring blew it big time; you gotta do thorough background checks to make sure that prospective applicants do not possess strong moral convictions.)
Unfortunately, the FDA does not control foreign medical journals, one of which decided to (shockingly) publish an article documenting the Pfizer trial fraud. Big whoopsie. This is why it is imperative to establish a unitary governing body for the whole world.
Source: Covid-19: Researcher blows the whistle on data integrity issues in Pfizer’s vaccine trial, The BMJ, 2 November 2021
IV-3. Study Rigging Option #3: Study Analysis
Once you finish the study itself, now it’s time to crunch the numbers from the study. Any problematic data that somehow got through all your protocol designs and sabotage will be cleaned up here. Think of this like giving a used dinged-up car a brand-new coat of paint to hide all the damage underneath; you’re not changing anything substantial, just disguising stuff (for the most part). Nobody wants to scratch the fresh new paint to make sure it’s not hiding something.
There are soooo many ways of “analysing” the data. The trick is to be smart about which ones you pick and how you go about doing the analysis.
Analysis Tactic #1: Don’t Adjust the Data
Data adjustments are pretty standard stuff in science. Raw data is almost never suitable for directly drawing inferences or extrapolating from, because there are usually all sorts of confounding variables present.
Here is a very simple example of a data adjustment:
The following is the population of the states of Darth Santistan (bad state) and The Gender Spectral Paradise of Commiefornia (good state):
Here are the death rates from the Dreaded Disease in these states. Overall, the bad state has more deaths than the good state. Since they have the same population, this means that the death rate is higher in the BAD, BAD state of Darth Santistan:
BUT … yes, there is a big “but” here …
If we look at the death rates for the senior population and non-senior populations separately, shockingly the good state has a higher death rate in BOTH (?!?!?!?!?):
Two important observations here.
The reason that the disloyal state of Death Santistan has a higher overall rate despite having lower death rates in each age cohort is very simple, actually. Seniors die far more often than non-seniors but the bad state has the misfortune of having 2.5x as many seniors as the good state, which means a lot more deaths overall because of the sheer number of senior citizens in the bad state of Death Santistan:
In order for the bad state to have the same number of senior deaths as the good state, they would have to have literally 40% of the death rate in seniors as the good state because the good state has only 40% as many seniors in their population as the bad state. This is why (when we want to be honest, like when the truth helps the regime) Science adjusts data – to avoid stuff like this. (This particular statistical phenomenon actually has an official name: “Simpson’s paradox.”)
Therefore, DON’T adjust the data when it will hurt the regime’s narrative.
Analysis Tactic #2: Adjust the Data Deceptively or Inappropriately
Conversely, sometimes the raw data, or properly adjusted data, will not be good for your narrative. In such cases, you gotta keep adjusting in creative ways until you’ve successfully obscured the heretical results so nobody can see them or figure them out.
For instance, if we take our above hypothetical comparison of the fictional states of the Gender Spectral Paradise of Commiefornia and Death Santistan, you can add an ‘adjustment’ to “fix” the problem. All you need to do is find a characteristic that is a proxy for worse outcomes in the Bad State of Death Santistan than the good state of Gender Spectral Paradise of Commiefornia. Since Death Santistan decided not to follow the regime’s Lifesaving Lockdowns, the seniors in Death Santistan tended to leave their houses more than other states, even if just to walk around the block for fresh air – meaning that seniors who didn’t leave their houses probably were more often too sick to leave their house. Such sick seniors are also more likely to be the ones who die from the Dreaded Disease.
Here’s how this could play out.
Chart #1 – population of seniors in each state (left columns = seniors who went outside at least once a week; middle = seniors who didn’t go outside; right = total number of seniors in each state):
Chart #2 – number of deaths in each of the three categories in Chart #1:
This completely fixes our problematic data (it might actually fix it too well!!) – observe how we are changing the death rate in seniors:
All you have to do now is to refer to the indoor senior death rate as the “population-adjusted senior death rate.”
Also, you could still refer to indoor senior deaths from time to time because it’s a lot easier to propagandise with a talking point like “seniors most at risk because they are immobile were almost THREE times as likely to die in the BAD state as they were in the GOOD state.” People naturally associate seniors with being stuck indoors, so they’re unlikely to appreciate that “indoor seniors” are in reality such a small percentage of our hypothetical Death Santistan senior population.
Analysis Tactic #3: Pick Optimal Endpoints
Endpoints are a big deal. Officially, the primary endpoint/s of a study is/are the central finding that determines whether the study is deemed a success or a failure. An endpoint is basically a thing or metric that you’re using to assess the success/failure or the impact of whatever it is you’re studying. For example, if you’re testing a new drug to see if it stops the Dreaded Disease from killing you, the endpoint would be Dreaded Disease deaths. If the treatment group had fewer Dreaded Disease deaths than the control group, then the treatment works, but if they didn’t, well, that means you didn’t rig the study well enough. (That’s a bit oversimplified but you get the basic idea.) So, you gotta make sure to choose wisely when picking the endpoint/s.
Therefore, you should generally pick endpoints that have as many of the following characteristics as possible:
- Depends on subjective judgment rather than objective observation.
- Naturally biased to your preferred results.
- Easy to manipulate the outcome.
- Easy to lie about the outcome.
- Hard for people to figure out if you falsified or manipulated the outcome.
- Hard to grasp/understand, especially for laypeople.
For example, let’s suppose you are running a trial for the purpose of sabotaging an alternative treatment that actually works on the Dreaded Disease (which would be very bad if the regime wants a pandemic crisis to be perpetuated for a while longer). You need to show it doesn’t work. If you pick “death” as an endpoint, you could get into big trouble when the drug saves a bunch of people in the treatment group.
Instead of death, you could pick something like “time to discharge from hospital.” This endpoint fulfils all six conditions (to some degree):
– Patient dischargement is a subjective decision by the doctors (who should be on the study’s payroll), so you’re not stuck releasing patients who meet an objective standard for release.
– Dischargement is biased to your preferred results. Since a higher percentage of the control group will die, this means that a higher percentage of severe cases never get discharged so they won’t increase the average time to discharge for the rest of the control group; compared to the treatment group where instead of dying, the more severely sick patients take a few extra days to recover, which increases the average time to discharge for the treatment group.
– Dischargement is very easy to manipulate. You can recruit the hospital staff involved in the study to unnecessarily delay dischargement of the treatment patients for a bit (you need to make sure that the relevant staff knows who got the treatment and therefore waits extra to discharge from the hospital).
– Time to dischargement is also fairly easy to falsify; just edit the paperwork for either the date admitted to the hospital and/or the date discharged (and the security footage if necessary). Death is much harder to falsify because the time of death is typically something recorded very accurately and appears on the death certificate.
– “Time to dischargement” is not the most intuitive metric to a layperson.
Obviously, you can do better for most of these conditions but this conveys the basic idea.
Analysis Tactic #4: Bury Alternate Endpoint Metrics
This one is practically self-evident: if you use “time to discharge” as the endpoint but report that there was a 50% reduction in mortality in the treatment group, well, let’s just say that will raise a lot of eyebrows.
So instead of having to face tough questions about why you chose such an absurd endpoint and why you would claim the treatment doesn’t work if you see that the treatment significantly reduced mortality, you should ideally not report the deaths anywhere in the study.
If you can’t avoid reporting the mortality statistics, at least you should bury them in the middle of a random table of an appendix in a format that is very difficult to make sense of. Or better yet, sprinkle them throughout multiple data tables instead of all-in-one place where it is easily identified by some annoying random nerd in his basement.
Analysis Tactic #5: Employ the Optimal Types of Analysis To Get Your Desired Results
There are as many ways to analyse data as there are gender identities or pronoun combinations. Unfortunately, an in-depth explanation of various methods cannot be distilled into a format appropriate for an Idiot’s Guide like this. Just look at some of these names:
- Balanced Design Analysis of Variance.
- Beta Distribution Fitting.
- Box-Cox Transformation for Two or More Groups (T-Test and One-Way ANOVA).
- Clustered Heat Maps (Double Dendrograms).
- Distribution (Weibull) Fitting.
- Fuzzy Clustering.
- Gamma Distribution Fitting.
- General Linear Models (“GLM”).
- Grubbs’ Outlier Test.
- Hierarchical Clustering/Dendrograms.
- K-Means Clustering.
- Medoid Partitioning.
- Multivariate Analysis of Variance (“MANOVA”).
- Nondetects-Data Group Comparison.
- One-Way Analysis of Covariance (“ANCOVA”).
- Regression Clustering.
The point is that different methods of statistical analysis will yield different results. If they didn’t give different results, then there wouldn’t be so many methods. It’s all a matter of perspective. So, you gotta hire yourself competent statistical gurus who know this stuff (and are loyal to the regime) for two reasons:
1. You get the benefit of their expertise (which you need; remember your expertise is propagandising, not fancy statistical analysis. A little practical humility in recognising your own limitations is crucial to being a successful propagandist; overconfidence has been the undoing of many a loyal regime lackey, and often also precipitated a lengthy vacation in an underwhelming Gulag).
2. Regime heretics cannot point to the lack of credible expertise of your statistical analysts to besmirch and impeach the credibility of regime studies. The case of Neil Ferguson stands as a cautionary tale. Although he initially succeeded in convincing governments around the world with his fabulous model predicting apocalyptic carnage from covid, his utter lack of any subject matter expertise plus his lengthy history of completely delusional pandemic predictions gave the opposition a firm basis to discard his models and all subsequent models pushed by various governments. They were also able to proselytise to great effect on the back of this debacle.
Analysis Tactic #6: Remove Problematic Data That Cannot Be Analysed, Adjusted or Otherwise Hidden
This is the same concept as kicking out subjects from a study if they are inconsistent with the regime’s mandated results; just here you’re removing the data already generated instead of the study subjects themselves. The objective is the same though: to prevent the data that doesn’t fit with what you want the study results to show from getting into the official record of the study in the first place.
IV-4. Study Rigging Option #4: Recruiting Media to Spin the Results
Regardless of what the results are, you should have ready-to-go talking points for sympathetic media outlets to go to bat for you. It doesn’t make a difference how false, misleading, etc. they are, the whole point of propaganda is to gaslight and mislead. The media simply by flooding the ecosphere with your information is a powerful force that will at minimum make it very difficult for most people to be able to unwind the lies and deceptions you are propagating rapidly throughout society.
You should be especially prepared to viciously target any scientist or academic with heretical leanings who might question anything you say, or worse, call attention to deficiencies in your study. With maximum prejudice.
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’.
This article (An Idiot’s Guide to Propaganda: How to cook the data (Part 4)) was published by The Expose and is republished here under “Fair Use” with attribution to the author Aaron Hertzberg
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