The 2024 Presidential election is over and many people, in both parties, are trying to figure out what happens next. While Republicans are considering what to do with the power that they’ve been handed, Democrats are trying to figure out how they were run over by the Trump train. Going into the elections, most polls suggested the race was a dead heat, with no more uncertainty than a coin toss. But they were wrong, and President Trump did not just win the 2024 Presidential election, he won big. Vice President Harris and the Democrats did not merely lose, they got thumped. So much the better, as far as I am concerned; the country is better off with less of what that party stands for. 

But how, many want to know, did so many pollsters miss such a lopsided outcome?

If you work in the social sciences, as I do, there is little mystery to this question. The answer is found in how most people view numbers and researchers’ blind spots.

Most people, researchers included, are unaware that they tend to see information expressed numerically as “truer” than that expressed as words. This is a strange bias. Numbers or words, spoken or written, are equal in that they are both sounds or symbols, and no set of sounds or symbols is any inherently truer than another. Nonetheless, this bias is what drives some people to wear t-shirts that read “I Believe in Science,” since science, it is assumed, is founded in numbers that are truer than words. But this is not true, and people, including the aforementioned t-shirt wearers, lie to themselves and others with numbers as much as they do with words.

This assumption that some representations (numbers v. words) are truer than others is central to researcher blind spots. As the name suggests, blind spots are biases that keep researchers from noticing problems with the data or the methods they use to analyze it. Some blind spots are a result of low competence, in that quantitative research can be complicated, and those who don’t know what they don’t know often blunder into mistakes. But even among well-trained researchers problems can go unchecked because, quite literally, researchers can’t or won’t admit to holding assumptions that aren’t true.

For example, a number of the polls that predicted the Vice President would win the election routinely asked greater numbers of Democrats than Republicans who they would vote for. On the surface level this is cheating, since if you ask 100 people who they will vote for, and 55 of them are Democrats and 45 of them are Republicans, then you will likely have a higher percentage of voters who favor the Democrat candidate. This process is called oversampling and is sometimes done to deliberately slant the outcome of a poll. Other times it is done, again, deliberately, with certain behavioral assumptions in mind.

In the 2024 Presidential election, many pollsters assumed that Democrats would turn out to vote in greater numbers than Republicans, and they oversampled their polling accordingly. However, what they did not see, or did see but ignored anyway, was that new voter registration heavily favored Republicans over Democrats, especially in crucial swing states. Vice President Harris was an ineffectual communicator, failed to distance herself from President Biden’s damaging policies and overall was a poor candidate. Pollsters refused to see the obvious; Democrat voters were not coming out to see Harris at her rallies, were not registering to vote in numbers higher than Republicans, and thus would likely not turn out to vote for her on election day.

I don’t doubt that some pollsters employed by the Democrats did notice the “we’re not happy with her” trend developing among voters, but telling your boss that “you’re unpopular and people don’t like you” can be difficult in most circumstances. It can be near impossible when your boss is Kamala Harris who is notorious for her profanity-filled-tirades and high staff turnover.

Predicting human behavior can be tricky, especially if you are certain, yet mistaken, about how to move people from where they are to where you want them to be. Add intense market pressures and the demands of big funders to blind spots, and you have the making of a nearly $2 billion failure that was the Harris campaign. FEC filings reveal that the Harris team spent millions paying for celebrity endorsements and social media influences, assuming that people like Oprah Winfrey (whose company, Harpo Productions, was paid $2.5 million by the Harris campaign) had the juice to move voters. The campaign was wrong in this assumption, wrong in their data models, and consequently wrong in their conclusion that Americans would vote for Harris. In statistics, there is an old saying when it comes to analyzing flawed data and expecting accurate results: “Garbage in, garbage out.”

While I believe that a loss on the 2024 scale for the Democrats is a win for the country, the problematic implications of “garbage in garbage out” in statistics extend well beyond this year’s election and pose a serious and ongoing risk to us all.

For nearly twenty years, throughout most of the social sciences and even some of the so-called hard sciences, there has been an uncomfortable and growing awareness of an inability to reproduce the results of tests that claim certainty about the cause and effect of a wide range of human behaviors. Among social scientists, this problem even has its own name, the replication crisis.

In plain terms, “replication crisis” means that in some areas of social science (including political science, communication, sociology, and psychology) somewhere between 30% and 70% of studies that pronounce conclusions about human behavior, when repeated, do not produce the same results. When any study of anything cannot be repeated with a similar outcome to the original study we should not draw any conclusions, based on that study, about the general behavior of objects or people in the world around us.

The trouble with being wrong, on average up to 70% of the time, is more than just an academic problem. Research in the social sciences can end up as the driving force behind large-scale social change, like how best to discipline our children, underpin corporate policies such as assumptions about the utility of DEI training, and propel governmental legislation which, for example, insists that (somehow) boys who feel they are girls have no advantage when competing in girls’ sports.

Combine junky social science or outright fraud with people’s assumptions that numbers are truer than words, and you have a potent spell with which to hypnotize the public and drive dramatic and damaging social engineering.

Consider the now-overturned Roe v. Wade. Back in the late 1960s and early 1970s, Dr. Bernard Nathanson (1926 – 2011) was a founding member of the National Association for Repeal of Abortion Laws (NARAL) and worked for years as an abortionist pushing for the legalization of abortion. No stranger to the procedure, Nathanson, by his admission, performed some 70,000 abortions, including one that took the life of his child. Shortly after the passing of Roe v. Wade in 1973, Nathanson watched, utilizing ultrasound, an abortion being performed. He was so shocked and sickened by what he saw that he never performed another abortion.

As part of his conversion, Dr. Nathanson revealed that the statistics he and others had used to change abortion laws were grossly exaggerated or simply false. These numbers admitted Nathanson, were nonetheless slavishly repeated by a compliant media and accepted by a public that never bothered to check. Only five years after Roe v. Wade, in 1979, Nathanson confessed that deception, writing that:

“How many deaths were we talking about when abortion was illegal? In NARAL (National Association for Repeal of Abortion Laws) we generally emphasized the drama of the individual case, not the mass statistics, but when we spoke of the latter it was always 5,000 to 10,000 a year. I confess that I knew the figures were totally false. But in the “morality” of our revolution, it was a useful figure, widely accepted, so why go out of our way to correct it with honest statistics? The official figures of maternal death due to illegal abortion before abortion was legalized was [likely] 160 per year.” 

Even after Nathanson, a trained and experienced physician, publicly and repeatedly stated that abortion was never medically necessary and revealed the fraudulent and manipulated data used to promote it, Americans did not want to hear what the doctor had to tell them. Like many researchers, the public had developed their blind spot, the assumption that abortion was needed, and refused to see beyond it. The legalization of abortion on a national scale dramatically changed the social, economic, and political direction of the country. 

More recently, consider another outstanding example of numerical deception to drive social change by the radical group, Black Lives Matter, or BLM.

In 2014, in Ferguson, Missouri, 18-year-old Michael Brown tried to take police officer Darren Wilson’s gun. Wilson, acting in self-defense, shot and killed Brown. This event not only gave rise to the “hands up, don’t shoot” campaign, but was a contributing factor to the formation of BLM and its riot-fueled push to defund police departments around the country. Even though a special investigation, organized by the Obama Justice Department, concluded that the shooting of Michael Brown was justifiable and that he had not been shot with his “hands up,” arson-fueled riots spread across Missouri and to other parts of the country. On the frontlines of many of these protests were the founder and future supporters of BLM.

At the heart of BLM’s raison d’être is the claim that police departments in any town-USA are systemically racist, proven, the organization says, by the nearly 1,000 people who are killed in America each year by the police. The solution to this problem, asserts BLM, is for cities around the country to defund their police departments, and nationwide, many cities did just that. Even in cities that did not reduce funding to their police departments, BLM-driven protests have so stigmatized the role of law enforcement that the total number of police officers has dropped dramatically. Not surprisingly, the incidents of violent crime have jumped as much as five-fold in cities that have seen a drop in the number of police officers willing to work there.

On its face, the 1,000 people killed each year seems shocking and, one might conclude, calls for some dramatic action like defunding the police. However, what BLM leaves out of that number is an explanation of who was shot by police and why.

According to data collected by the Federal Bureau of Investigations, and republished by the Washington Post, between 2015 and 2023 there were 65 million encounters with police nationwide. Of those 65 million encounters, 8,166 ended with someone shot and killed by the police. Of those 8,166 killed, only 463 were unarmed. The rest were shot and killed because they were wielding some sort of a weapon: a blunt object, a gun, or a knife, or driving a vehicle in a manner that threatened the public or a police officer’s safety.

Thus, by the numbers, from 2015 to 2023, of the 65 million encounters with police officers, for all races, less than 0.000007 of them ended in a deadly shooting that did not involve a person threatening an officer or the public. When we look only at black Americans in those numbers, we find a fatality rate of 6 people per one million, or 0.0006 percent. This number is very, very low. To put that number in perspective, the aviation fatality rate (people who die in plane crashes) in the US in 2023 was 0.03 per one million flights, which is significantly higher than one’s chances of being unarmed and killed by the police, regardless of one’s race.

How should we react to the aviation numbers? Should we riot and burn down our airports? Should we demand that we defund aviation? If we follow the data-manipulated logic of organizations like BLM, that answer would be “yes.”

Lastly, and perhaps the most recently egregious example of data distortion and public manipulation can be found during the COVID-19 pandemic years of 2020 to 2022.

Starting in 2021 and repeated in 2022, health organizations, the media, and even President Biden told the public that the COVID-19 pandemic was a “pandemic of the unvaccinated,” saying that if all people were vaccinated, there would not be a pandemic.

The problem with this claim is that early on in the pandemic health organizations around the world were reporting high numbers of COVID-19 cases among the vaccinated. In some countries, like Germany, data from late 2020 and early 2021 showed that over 50% of reported COVID-19 infections were among those who had been vaccinated. Yet the push for universal vaccination roared on. Why? Well, perhaps the fact that the three largest producers of COVID-19 vaccinations, Pfizer, BioNTech, and Moderna, made an astounding $65,000 a minute throughout 2021 might account for some of the push for universal vaccination.

A number of governments have certainly concluded as much, and some, like the State of Texas, believe that Pfizer’s primary focus during the pandemic was profit and not public health. In late November of 2023, the Attorney General for the State of Texas filed suit against Pfizer, Inc. for fraud and misrepresentation of their product, the COVID-19 vaccination. In the suit, which seeks to recapture the millions of taxpayer dollars given to Pfizer for the vaccine, the State of Texas notes that Pfizer claimed the vaccine was 95% effective 28 days after vaccination. But internal data from Pfizer, the State of Texas points out, indicates that the company knew that “preventing one COVID-19 case required vaccinating 119 [persons].” This is an efficacy rate of 0.85%, which is considerably lower than 95%. Gaps of this size in the data cannot be due to a calculation error but are instead the result of flat-out fraud.

Without question the system is broken, or at best, not working well, and presently much of what experts tell us about who we are and why we do what we do is either misguided or just wrong. As dire as this seems, the news is not all bad. Happily, half of how “the system” works is what we, the public, do or do not accept as “fact” and how we act out either our acceptance or rejection of the message. 

To keep ourselves from being whip-sawed by social manipulators who use facts, especially in the form of numbers generated by this study or that, let’s stay grounded in a few immutable truths. First, we are all flawed, fallen beings in need of salvation which can never come from a statistician. Second, time-tested wisdom and commonsense social norms more often help us than they hold us back. True, societies have developed patterns of behavior that can rightly be called “bad behaviors,” like the notion that some humans are not persons and thus can be owned, think slavery, or chopped up and sold, as in the case of abortion. But these behaviors, over the arc of thousands of years of Western Civilization, are the exception and not the rule. Most of what we do as a society, including things like gender roles, developed, and continue to develop, for a reason, and we should be very cautious about overturning useful and stabilizing patterns of behavior. Whether you are running a national presidential campaign or just your own life, be especially cautious, if not downright skeptical, when confronted with a researcher who, waving some newly discovered number, has jumped up from the tepid water of his work and is running wild-naked down the street crying out “Eureka, I’ve got it!”