Chapter 9. Comparing Data Using Statistical Tests

  1. Clemens Reimann M.Sc. in Mineralogy and Petrology, Ph.D. in Geosciences, D.Sc. in Applied Geochemistry lecturer senior geochemist director professor chairman acting vice president associate editor1,
  2. Peter Filzmoser Applied Mathematics visiting professor2,
  3. Robert G. Garrett Mining Geology and Applied Geochemistry Emeritus Scientist3 and
  4. Rudolf Dutter M.Sc., Ph.D. senior statistician full professor post-doctoral fellow2

Published Online: 18 APR 2008

DOI: 10.1002/9780470987605.ch9

Statistical Data Analysis Explained: Applied Environmental Statistics with R

Statistical Data Analysis Explained: Applied Environmental Statistics with R

How to Cite

Reimann, C., Filzmoser, P., Garrett, R. G. and Dutter, R. (2008) Comparing Data Using Statistical Tests, in Statistical Data Analysis Explained: Applied Environmental Statistics with R, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470987605.ch9

Author Information

  1. 1

    Geological Survey of Norway, Norway

  2. 2

    Vienna University of Technology, Austria

  3. 3

    Geological Survey of Canada, Canada

Publication History

  1. Published Online: 18 APR 2008
  2. Published Print: 4 APR 2008

ISBN Information

Print ISBN: 9780470985816

Online ISBN: 9780470987605

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Keywords:

  • statistical test - data comparison;
  • Kolmogorov–Smirnov and Shapiro–Wilk tests;
  • geochemical data and lognormal distribution;
  • one-sample t-test;
  • Wilcoxon signed-rank test and Wilcoxon rank sum test;
  • two data set variance comparison;
  • Ansari–Bradley test;
  • Bartlett test - data group equality variance;
  • notched Tukey boxplot comparison

Summary

This chapter contains sections titled:

  • Tests for distribution (Kolmogorov–Smirnov and Shapiro–Wilk tests)

  • The one-sample t-test (test for the central value)

  • Wilcoxon signed-rank test

  • Comparing two central values of the distributions of independent data groups

  • Comparing two central values of matched pairs of data

  • Comparing the variance of two data sets

  • Comparing several central values

  • Comparing the variance of several data groups

  • Comparing several central values of dependent groups

  • Summary