Analytical and Bioanalytical Chemistry

, Volume 382, Issue 2, pp 320–327 | Cite as

Robust and non-parametric statistics in the evaluation of figures of merit of analytical methods. Practices for students

  • M. Cruz Ortiz
  • Ana Herrero
  • Silvia Sanllorente
  • Celia Reguera
Special Issue Paper

Abstract

A set of laboratory practices is proposed in which evaluation of the quality of the analytical measurements is incorporated explicitly by applying systematically suitable methodology for extracting the useful information contained in chemical data. Non-parametric and robust techniques useful for detecting outliers have been used to evaluate different figures of merit in the validation and optimization of analytical methods. In particular, they are used for determination of the capability of detection according to ISO 11843 and IUPAC and for determination of linear range, for assessment of the response surface fitted using an experimental design to optimize an instrumental technique, and for analysis of a proficiency test carried out by different groups of students. The tools used are robust regression, least median of squares (LMS) regression, and some robust estimators as median absolute deviation (m.a.d.) or Huber estimator, which are very useful as an alternatives to the usual centralization and dispersion estimators.

Keywords

Proficiency test Non-parametric statistics Least median of squares regression Linear range Capability of detection Experimental design 

Notes

Acknowledgements

The authors thank the Junta de Castilla y León (project UB20/02 and UB14/04) for financial support.

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Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • M. Cruz Ortiz
    • 1
  • Ana Herrero
    • 1
  • Silvia Sanllorente
    • 1
  • Celia Reguera
    • 1
  1. 1.Department of ChemistryUniversity of BurgosBurgosSpain

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