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Two Samples

  • Edgar Brunner
  • Arne C. Bathke
  • Frank Konietschke
Chapter
Part of the Springer Series in Statistics book series (SSS)

Abstract

This section introduces nonparametric methods for two independent samples. These describe observations on n1 individuals (subjects, experimental units) in one group, and on n2 other individuals in another group. The groups could correspond to different treatments to which the subjects are randomly assigned, or they could refer to different sub-populations (e.g., male vs. female). Mathematically, this situation is modeled by each of the two samples consisting of ni independent and identically distributed random variables \(X_{i1}, \ldots , X_{in_i}\), i = 1, 2, and by assuming independence across groups. Using the unified nonparametric approach described in this section, it is not necessary to consider the cases of continuous and discrete data separately. Thus, a correction for ties is not necessary—a technique that often had to be applied in the classical framework of nonparametric statistics. The methods described here are valid for data with or without ties, specifically for continuous, quantitative data, count data, ordinal data, and even binary (dichotomous) data. Real data examples illustrate each of these cases. The corresponding data analyses are demonstrated using R and SAS. In the subsequent Chap.  4, the results presented here for two samples (a = 2) are generalized to more than two samples (a ≥ 2).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Edgar Brunner
    • 1
  • Arne C. Bathke
    • 2
  • Frank Konietschke
    • 3
  1. 1.Department of Medical StatisticsUniversity of G¨ottingen, University Medical CenterGöttingenGermany
  2. 2.Department of MathematicsUniversity of SalzburgSalzburgAustria
  3. 3.Institute of Biometry and Clinical EpidemiologyCharité – University Medical SchoolBerlinGermany

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