Why are Normal Distributions Normal?
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 bi...
| Publicado en: | British Journal for the Philosophy of Science Vol. 65; no. 3; pp. 621 - 650 |
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| Formato: | Artículo |
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University of Chicago Press
Sep2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=97421756&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 97421756 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00070882 BPL jtl: British Journal for the Philosophy of Science issn: 00070882 maglogo: N pubinfo: dt: Sep2014 vid: 65 iid: 3 pid: 415 pub: University of Chicago Press artinfo: ui: 97421756 10.1093/bjps/axs046 ppf: 621 ppct: 29 formats: tig: atl: Why are Normal Distributions Normal? aug: au: Lyon, Aidan affil: Department of Philosophy, University of Maryland, College Park, MD, USA su: Gaussian distribution Central limit theorem Probability density function Tensile strength Random variables Snowflakes sug: subj: Gaussian distribution Central limit theorem Probability density function Tensile strength Random variables Snowflakes ab: 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 Introduction2 Normal Distributions and the Central Limit Theorem 2.1 Normal distributions 2.2 The central limit theorem 2.3 Terminology3 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 explanation4 Maximum Entropy Explanations5 Conclusion pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2014 holdings: @attributes: islocal: N |
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