Rethinking the Bell Curve A Multi Dimensional Approach to Understanding Populations
Year: 2025
Type: article
Abstract: The bell curve (normal distribution) has long been used in statistics, psychology, and social sciences to model human populations. However, traditional one-dimensional applications of the bell curve oversimplify reality by reducing complex human traits—such as intelligence, social mobility, and economic status—into a single axis. This approach fails to account for interdependent variables, systemic biases, and dynamic human development over time.This paper proposes a multi-dimensional statistical model that extends the bell curve into a multi-trait, evolving probability distribution. By incorporating multivariate normal distributions and Mahalanobis distance, we demonstrate how multi-dimensional modeling better captures individual variation, interdependent human traits, and systemic complexity.We explore the flaws of traditional models, the mathematical underpinnings of multi-dimensional probability spaces, and the real-world consequences of shifting to a dynamic framework. Key implications are discussed in the fields of education policy, hiring practices, social stratification, and artificial intelligence, emphasizing how AI-driven decision-making can benefit from a more holistic understanding of human traits.
Source: Figshare
Author: Hall, Matthew
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: green
OpenAlex ID: W6902227172
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