A great mystery: Why do we keep trusting government data?

First the government manipulated inflation and unemployment data … then bureaucrats really showed off with their Covid data.

In this article, I touch only lightly on the motive for manipulating data … but this might be one of them.

Somewhere in my life’s journey, I became a “contrarian” and started to question many of the settled truths imparted to me by experts, including economists.

For example, I quit trusting the “official data” of two important economic measures - the unemployment and CPI (inflation) figures.

“Garbage in/garbage out.”

To use software parlance, I reached the conclusion that garbage (or dubious) inputs produced garbage (or dubious) outputs.

In my view, the more accurate numbers for both CPI and unemployment are much higher than experts and authorities tell us.

For years, I read economics stories telling me annual CPI was hovering around 2 percent. However, per my observations, the price of every widget and service I had to buy was increasing at a much higher rate.

Even today, when the experts admit inflation is high, I know the real figure is much higher.

“Yeah, right.”

The same skepticism applies to official unemployment numbers, where I know far more people are unemployed than the government says.

Regarding both unemployment and CPI statistics, not enough citizens are aware the methodologies used to provide these economic metrics have been modified many times.

Regarding unemployment, the “participation rate” of people in the labor force is the much-better measure.

The unemployment rate - in recent years, usually around 3 to 6 percent - is kept low by simply not counting the number of people who have “dropped out of the labor force.”

Per the operative metric, these people aren’t “unemployed” because they are no longer looking for employment.

Essentially, the government changed the definition of unemployed.

Government statisticians did the same thing with a series of tweaks to the CPI data.

A better measure of inflation might be the number of “work-arounds” families now have to utilize to ensure they have a positive balance in their checking accounts at the end of the month.

For example, raise your hand if you’ve done away with the family maid service … dropped out of a country club or civic club … no longer wear clothes that require dry cleaning … now eat chicken much more often than beef … or if you’ve “cut the cord” on local cable TV.

If you or your family made any of these changes, you probably didn’t do them because you were convinced inflation was “low and contained.”

Another alternative way to calculate real inflation might be to simply tally all the packages and products that have undergone significant “shrinkflation.”

That is, what’s the size of a candy bar or the number of ounces in a can of soup trying to tell us about real inflation?

Trusting the government with its Covid data …

My hypothesis that government officials routinely manipulate data by changing definitions was reinforced in the Covid years.

For example, the most-cited Covid metric was the number of Covid “cases.”

We all remember the ever-rising case numbers that scrolled across the bottom of our TV screens.

In practically every Covid article, the first two paragraphs would include information on the most-recent “case” numbers in the country, state or your city.

It was the case numbers - and then the Covid “death” numbers - that fueled the mass panic that convinced the vast majority of citizens we all had to do our part to fight this rapidly-spreading and “deadly” disease.

Alas, few citizens ever questioned the veracity of these numbers. They just knew these figures came from public health experts in the government so they must be true.

(In fact, many of these statistics were provided to state health agencies or the CDC by independent contractors like Johns Hopkins University - so, often, “government” case numbers weren’t even produced by the government.)

*** (One of my key metrics is my “share” statistic.) ***

The definition of a ‘case’ was changed …

Similarly, few citizens realized the definition of a “case” had quietly been changed.

Once upon a time, a “case” was short-hand for a “medical-case,” meaning the person who was listed as a case had gone to the hospital or doctor’s office complaining of a defined set of symptoms that required medical attention.

Thus, this person became a “medical case.”

In Covid, the first word - “medical” - was simply dropped … or, sneakily, it was inferred that if you were a case you must have required some kind of medical intervention.

As early as February 2020, a “Covid case” simply had to be someone who tested positive via a new test, the PCR test (which all Covid contrarians learned have “cycles” that can be manipulated.)

It turns out almost everyone who tested positive on this test didn’t even have any symptoms (unless sniffles or a scratchy throat were counted).

Even today, the percentage of “asymptomatic cases” of Covid is not well-established. It might be 51 percent of people who got the test; but it might be 91 percent. (Results also depended on whether the test used 25 or 40 cycles to reach the “positive” part of the dial.)

Aside: In reading early Covid stories, I gleaned some of my best information from articles written by sports journalists - as members of pro and college teams were forced to receive these tests at least three times a week.

Invariably, I’d learn that 3, 10 or 15 athletes had “tested positive.” Sometimes, however, the reporter would mention that “all cases were asymptomatic” or “most cases were asymptomatic.”

Such anecdotes qualified as news I could use. These were Covid cases who, in fact, never became “sick.”

In fact, from my “early spread” research, I’m 100-percent convinced the number of athletes - and non-athletes - who were “sick” in the pre-Covid months of November and December 2019 through January and February 2020 dwarfed the number of people who became “sick” in the post-Covid months of March through June 2020.

That is, Covid became the first global pandemic in history where almost everyone who was classified as a “case” … never got sick.

Still, the official data did its intended job of scaring the living daylights out of 95 percent of the global population.

The ‘death’ numbers were even scarier …

Of course, Covid “deaths” received even more media attention than Covid “cases” - but the death numbers were massively inflated as well - by changing the definition of a “cause of death.”

Again, the PCR test was used to change the medical definitions.

If someone had tested positive at any point before dying, this person was classified as a Covid “death”/victim.

As reported in footnotes in medical journals few people read, it turned out a typical Covid victim had, on average, almost four co-morbid conditions.

In Europe, which kept and reported better records, a few citizens learned the average age of a “Covid victim” was 82 - a statistical tidbit which suggests that “old age” might have been the culprit in most of these deaths.

(One of the hundreds of conspiracies required to pull off the Pandemic of the Century required MSM journalists to take an oath never to mention the average age of a Covid victim.)

Even today, probably less than two percent of the population could report they knew someone under the age of 65 who “died from Covid.”

Certainly, 99.99 percent of the population couldn’t identify a single child or college student in their towns who “died from Covid.”

Still, per the official records, millions of people suddenly “got Covid” and more than 350,000 Americans allegedly died from this disease between the months of March and December 2020.

Bizarrely, the death tally sky-rocketed in 2021 - the second year of Covid and after 70 percent of the country had been “vaccinated.”

While it turned out the “safe and effective” vaccines didn’t actually prevent “cases,” they at least were supposed to prevent death … but, apparently, they didn’t.

Rigging the vaccination numbers …

In early 2021, the media and every public figure who’s more moral than thou transitioned to an effort to besmirch all citizens who refused to get their Covid shots.

In 2021-2022, “A Pandemic of the Unvaccinated” became a more ubiquitous phrase than “Beat Back the Hun” was in 1941-1945.

But the data distinguishing between “vaccinated” or “unvaccinated” was also significantly and duplicitously manipulated.

If someone hadn’t received two Covid shots when he or she died, he was considered “unvaccinated.”

If the hospital didn’t ask or didn’t have records regarding a patient’s vaccination status, this person, by default, was labeled “unvaccinated.”

People who died from injuries suffered in a car crash and who’d tested positive with a PCR test and hadn’t gotten both of their shots could (and were) labeled “unvaccinated victims of Covid.”

In summary …

Covid case, death and vaccination numbers were all highly suspect and/or intentionally manipulated by government-reported data.

It turns out that organizations that control the data have the motive and the means to create any narrative they choose.

If this official data scares people enough to voluntarily discard civil liberties veterans died to protect, so be it. Indeed, scaring as many people as possible via rigged data might have been the key to the entire operation.

In America, citizens have been conditioned to “trust the data” just like we’re supposed to “follow the science.”

But most Americans don’t think about the entities that generate this data (or the motives of those who perform said “science.”)

The same government that told us inflation was “low and contained” told us Covid case numbers were sky-rocketing … which meant everyone had to get their Covid vaccines - which weren’t really “vaccines” after all … which is yet another definition our trusted officials had to change.

*** (Total subscriber numbers on Substack might also be rigged, but I won’t get into that today.) ***

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