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 HerreroEmail author
  • Silvia Sanllorente
  • Celia Reguera
Special Issue Paper


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.


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



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


  1. 1.
    González J, Wagenaar R (eds) (2003) Tuning educational structures in Europe. Final report. Pilot project, Phase one. University of Deusto, BilbaoGoogle Scholar
  2. 2.
    Moore J (1989) J Chem Educ 66:15–19Google Scholar
  3. 3.
    Statgraphics Plus for Windows 4.0. Statistical Graphics CorporationGoogle Scholar
  4. 4.
    Rousseeuw PJ, Leroy AM (1987) Robust regression and outlier detection. Wiley, New YorkGoogle Scholar
  5. 5.
    Sarabia LA, Ortiz MC (1994) Trends Anal Chem 13:1–6Google Scholar
  6. 6.
    Mathieu D, Nony J, Phan-Tan-Luu R (2000) NEMRODW Version 2000. LPRAI, MarseilleGoogle Scholar
  7. 7.
    Thompson M, Wood R (1993) J AOAC Int 76:926–940Google Scholar
  8. 8.
    International Standard Organization ISO 5725-5 (1998) Accuracy (trueness and precision) of measurements methods and results. Alternative methods for the determination of the precision of a standard measurement method. ISO, Geneva, SwitzerlandGoogle Scholar
  9. 9.
    Analytical Methods Committee. Robust Statistic Part I & II (1989) Analyst 114:1693–1702Google Scholar
  10. 10.
    Draper N, Smith H (1981) Applied regression analysis, 2nd edn. Wiley, New YorkGoogle Scholar
  11. 11.
    Hampel FR, Ronchetti EM, Rousseeuw PJ, Stahel WA (1986) Robust statistics. The approach based on influence functions. Wiley, New YorkGoogle Scholar
  12. 12.
    International Standard Organization ISO 11843-1 (1997) Capability of detection: terms and definitions. ISO, Geneva, SwitzerlandGoogle Scholar
  13. 13.
    International Standard Organization ISO 11843-2 (2000) Capability of detection: methodology in the linear calibration case. ISO, Geneva, SwitzerlandGoogle Scholar
  14. 14.
    Inczédy J, Lengyel T, Ure AM, Gelencsér A, Hulanicki A (1998) IUPAC, Compendium of analytical nomenclature, 3rd ed. Blackwell, OxfordGoogle Scholar
  15. 15.
    Lewis GA, Mathieu D, Phan-Tan-Luu R (1999) Pharmaceutical experimental design. Marcel Dekker, New YorkGoogle Scholar

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

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

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