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<!DOCTYPE html>
<html lang="en">
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<meta charset="UTF-8">
<title>Introduction - Data's Legacy</title>
<link rel="stylesheet" href="intro_conclusion.css">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<h1>Introduction</h1>
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<section>
<p>
Why do statistical tools so often fail to capture the unpredictable essence of human behavior? Time and again, data-driven predictions—like those for election outcomes—miss the mark, raising questions about how entire demographics, seemingly bound by patterns, can break from predicted narratives. Or consider the metrics we use to define happiness, intelligence, and success: Do they truly illuminate human well-being, or do they obscure the complexity of individual lives? Beneath their veneer of objectivity, these metrics rely on methods that reduce human diversity to neat patterns and probabilities. </p>
<p>
Data analysis thrives on identifying trends, but its dependence on averages and aggregates inevitably sacrifices nuance. Unique experiences are smoothed out, and outliers are dismissed as anomalies. Even well-intentioned visualizations can flatten people’s lived realities into reductive narratives—a potent force for shaping public perception and policy. Too often, these tools’ hidden biases reflect the cultural and philosophical assumptions of their creators rather than the genuine complexity of those they claim to measure. </p>
<p>
Consider the World Happiness Report, which ranks nations by select criteria. Its tidy conclusions suggest clarity, but can a handful of metrics capture the profound and deeply subjective nature of happiness? Election forecasts similarly group voters by demographics and make sweeping predictions—yet they often fall short, revealing as much about the biases in our metrics as about real-world behavior. </p>
<p>
This project examines the historical and philosophical roots of these frameworks, focusing on early statisticians like Francis Galton, Karl Pearson, and Ronald Fisher. Their innovations gave scientific cover to personal prejudices, transforming subjective values into so-called objective rankings. By illuminating how these tools evolved—from eugenic hierarchies to modern AI-driven systems—we will question what is lost when human worth is reduced to numbers. Ultimately, this exploration encourages us to look beyond the charts and averages, so we may adopt frameworks that genuinely recognize the richness of human lives. <p>
Continue reading: <a href="eugenic-beginnings.html">Eugenic Beginnings</a>
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