Why are Normal Distributions Normal
Bibliographic Data
| ID | 8399763 |
|---|---|
| Authors | Aidan Lyon (0009-0003-0546-4584, University of Maryland, College Park, corresponding author) |
| Year | 2014 |
| Volume | 65 |
| Issue | 3 |
| Pages | 621-649 |
| Publication date | 2014-09-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | The British Journal for the Philosophy of Science (JOURNAL) |
| Journal identifiers | ISSN: 0007-0882 • E-ISSN: 1464-3537 |
| Publisher | Oxford University Press (PUBLISHER • GB) |
| DOI | 10.1093/bjps/axs046 |
| OpenAlex | W2090299879 |
| Language | EN |
| Citations received | 11 |
| References cited | 19 |
It is usually supposed that the central limit theorem explains why various quantities we find in nature are approximately normally distributed—people's heights, examination grades, snowflake sizes, and so on. This sort of explanation is found in many textbooks across the sciences, particularly in biology, economics, and sociology. Contrary to this received wisdom, I argue that in many cases we are not justified in claiming that the central limit theorem explains why a particular quantity is normally distributed, and that in some cases, we are actually wrong. 1 Introduction 2 Normal Distributions and the Central Limit Theorem 2.1 Normal distributions 2.2 The central limit theorem 2.3 Terminology 3 Explaining Normality 3.1 Loaves of bread 3.2 Varying variances and probability densities 3.3 Tensile strengths and problems with summation 3.4 Products of factors and log-normal distributions 3.5 Transforming factors and sub-factors 3.6 Transformations of quantities 3.7 Quantitative genetics 3.8 Inference to the best explanation 4 Maximum Entropy Explanations 5 Conclusion
Calculus (dental) · Central limit theorem · Limit (mathematics) · Mathematical analysis · Mathematical economics · Normal distribution · Normality · Physics · sort · Statistical physics · Statistics · Mathematics · Philosophy and History of Science
Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes
Zipf’s word frequency law in natural language
Galton, reversion and the quincunx
Diversifying the picture of explanations in biological sciences
Model Transfer and Universal Patterns
In defence of explanatory realism
Knowledge transfer across scientific disciplines
Explaining individual differences
Statistical Autonomous Explanations and the Patterns of Nature
The Mix Matters
Modelling Inequality
| Unique citing works | 11 |
|---|---|
| Citations per year | 0,92 |
| Citation span | 2014 - 2025 (12) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 11 |