Non-Parametric Statistics


The birth of non-parametric statistics is historically related to the solution of methodologic problems in experimental psychology. It was Stanley S. Stevens (1906-1973) who solved the question about the inappropriate use of measurement scales; he also proposed a new classification that gave rise to the distinction between nominal scales, rank scales, interval scales and continuous scales, a distinction we introduced in Chapter 2 (see Table 2.1). Based on this, behavioral science statistics was developed in the 1940s, in part thanks to other researchers such as Quinn McNemar (1900-1986), Frederick Mosteller (b., 1916) and Anthony W.F. Edwards (b., 1935), with a large use of non-parametric methods [CARACCIOLO, 1992]. Moreover, non-parametric statistics is also the result of a broader discussion between the founding fathers of Theoretical Statistics and the founding fathers of Modern Statistics (see Introduction to Chapter 4).


Fisher Exact Test Binomial Test McNemar Test Pectoral Muscle Iodinate Contrast Agent 
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  1. Altman DG (1991) Practical statistics for medical research. London. Chapman & Hall, pp 210–212Google Scholar
  2. Armitage P, Berry G (1994) Statistical methods in medical research. 3rd edn. Oxford. BlackwellGoogle Scholar
  3. Boehm T, Willmann JK, Hilfiker PR et al (2003) Thin-section CT of the lung: does electrocardiographic triggering influence diagnosis? Radiology 229:483–491PubMedCrossRefGoogle Scholar
  4. Caracciolo E (1992) Introduction to the 2nd italian edn. of: Siegel S and Castellan NJ jr. Statistica non parametrica: Milan. Mc-Graw-HillGoogle Scholar
  5. Conover WJ (1999) Practical nonparametric statistics. 3rd edn. New York. WileyGoogle Scholar
  6. Greenhalgh T (2006) How to read a paper. The basics of evidence-based medicine. 3rd edn. Oxford. BMJ books, Blackwell, p 79Google Scholar
  7. Leisenring W, Pepe MS, Longton G (1997) A marginal regression modelling framework for evaluating medical diagnostic tests. Stat Med 16:1263–1281PubMedCrossRefGoogle Scholar
  8. Leisenring W, Pepe MS (1998) Regression modelling of diagnostic likelihood ratios for the evaluation of medical diagnostic tests. Biometrics 54:444–452PubMedCrossRefGoogle Scholar
  9. McNemar Q (1969) Psychological statistics. 4th edn. New York. WileyGoogle Scholar
  10. Sardanelli F, Zandrino F, Imperiale A et al (2000) Breast biphasic compression versus standard monophasic compression in x-ray mammography. Radiology 217:576–580PubMedGoogle Scholar
  11. Siegel S, Castellan NJ jr (1992) Statistica non parametrica: 2° edizione italiana a cura di Caracciolo E. Milan. Mc-Graw-HillGoogle Scholar
  12. Soliani L (2007) Statistica applicata alla ricerca e alle professioni scientifiche. Manuale di statistica univariata e bivariata. Parma. Uninova-Gruppo Pegaso; 9: 1–2 ( Scholar
  13. Willmann JK, Weishaupt D, Lachat M et al (2002) Electrocardiographically gated multi-detector row CT for assessment of valvular morphology and calcification in aortic stenosis. Radiology 225:120–128PubMedCrossRefGoogle Scholar
  14. Yates F (1934) Contingency tables involving small numbers and the test χ2. J R Stat Society Suppl 1:217–235Google Scholar

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