Evolution is deeply connected to personal choices. In choosing partners, seeking opportunities, and shaping our lives, we subtly influence the traits passed on to future generations. Over time, this gradual and natural process has been woven into the fabric of our species.
However, a dangerous ideology emerged when the selective methods used for livestock or crops were applied to human populations. The categorization of people into groups labeled as “fit” or “unfit” laid the foundation for eugenics—a movement that sought to reshape humanity based on so-called scientific principles. This ideology was deeply intertwined with the development of statistical tools validated its assumptions and lent it legitimacy.
Francis Galton—half-cousin to Charles Darwin—was the driving force behind linking statistical analysis to human evolution. He believed that human progress could be accelerated through systematic breeding and saw quantitative methods as the key to achieving this vision. Using tools like regression and correlation, Galton sought to measure and rank human traits, embedding subjective judgments into what appeared to be objective frameworks. Convinced of his mission to improve humanity, he anchored these ideas in numbers, giving his push for controlled selection a veneer of scientific legitimacy.
The Paradox of Statistical Innovation
The critical flaw in Galton’s thinking was not just his personal biases but his assumption that human complexity could be collapsed into neat classifications for a single vision of progress. Paradoxically, this flawed foundation spawned statistical tools that revolutionized science. From Galton’s prototypes to refinements by Karl Pearson and Ronald Fisher, these methods propelled advances in medicine, genetics, and public health. In these domains, sorting and categorizing data is necessary. Yet the same analytical approach becomes problematic when used to differentiate entire groups—cast as superior or inferior, oppressed or oppressor—under claims of neutrality.This paradox repeats across eras and disciplines. Figures like Pearson and Fisher advanced statistical methods that deepened our understanding of human variation, yet these same methods have been wielded to reinforce biases and rationalize policies that marginalize those who don’t fit neatly into predefined categories. As the tools become more complex, their mathematical authority deepens the illusion of neutrality, making the embedded assumptions harder to detect. Untangling this paradox grows increasingly challenging as these tools continue to shape decisions across domains, from scientific research to algorithms that influence modern policy.
The Eugenics Tree: Symbol and Legacy
How did this drive to define and reshape populations gain authority? Part of the answer lies in what’s conspicuously missing from the famous Eugenics Tree, created for Second International Congress of Eugenics (September 25-27, 1921), held at the American Museum of Natural History, New York City. This emblem of the eugenics movement showed roots labeled with genetics, statistics, and sociology—supporting branches that promised an idealized human future. Yet the tree notably omits philosophical or ethical grounding, allowing fundamental questions about its broader implications to go unanswered, or not worth even considering. This gap illustrates a pervasive belief among eugenicists: human groups can and should be ranked without challenging the validity of those categories or reckoning with their far-reaching impact.
The Eugenics Tree, featured at the 1921 Congress, has since become a potent emblem of eugenic thought. Source: Prof Joe Cain.
To Galton, studying and engineering population-level variation was simply an extension of natural processes. His statistical accomplishments became instruments for ranking, labeling, and assigning value to human traits—whether intelligence, “fitness,” criminality, creativity, or moral character. Though we may dismiss Galton as a relic of a bygone era, he saw himself as a benefactor of progress, much like many who continue to deploy the methods derived from his work. While these tools may appear neutral, the way they segment data inherently opens the door to misuse, allowing societal biases to take root under an empirical facade.
Kantsaywhere: “Race Over Individual”
Galton’s unpublished utopian novel, Kantsaywhere, reveals the deeply personal nature of his vision: a society governed by strict hierarchies, shaped by his biases, and enabled by the very statistical tools he developed to measure and rank human traits. These tools not only reflected his beliefs but actively gave them a framework for implementation, making them more than just theoretical ideals. By contrast, an individual inspired by a fictional utopia like Marvel’s Wakanda may draw a sense of cultural pride and personal empowerment without necessarily adopting or imposing rigid categorizations onto others. The distinction lies in the tools: while Galton’s methods actively reinforced hierarchical divisions, cultural narratives like Wakanda’s celebrate identity and achievement without inherently prescribing a system of superiority. However, even in such empowering narratives, there lies a potential risk—when pride in one’s identity shifts from personal celebration to a collective framework that mirrors the same rigid distinctions it aims to transcend.
A page from Galton's manuscript of Kantsaywhere, showing his ideas on “racial fitness.” Source: UCL Special Collections.
The belief that collective goals can justify categorizing and controlling individuals remains deeply ingrained. This mindset has evolved into frameworks where categories like race are tied to narratives of privilege, oppression, and social outcomes. These frameworks, often accepted without question, seem to offer clarity or progress but instead create rigid structures that simplify the intricate and diverse nature of human experience. Questioning these frameworks, particularly around sensitive topics like privilege or oppression, often results in further categorization—where dissent itself becomes a label to be silenced rather than a perspective to be explored. Such is the power of categorization to reinforce its own authority. It’s only by addressing these loops of categorization and bias that we begin to comprehend their force in shaping both social norms and our sense of identity.
At the heart of these practices lies a statistical force that fundamentally changed scientific thought: the pull toward the average. Tools like regression and correlation, pioneered by Galton, were developed to measure variation but also have reinforced a gravitational pull toward normalization. This fixation on averages shaped how we define “normalcy” and cemented the idea that deviations from the norm require correction. While these tools have advanced many fields, their underlying assumptions continue to influence how we categorize and analyze human lives, shaping not only scientific practices but societal values as well. Continue reading: Magnetic Regression.