Skip to main content
SpringerLink
Log in
Book cover

Statistical Methods for Data Analysis in Particle Physics pp 123–136Cite as

Hypothesis Tests

  • Chapter

  • 2537 Accesses

Part of the book series: Lecture Notes in Physics ((LNP,volume 909))

Abstract

A key task in most of physics measurements is to discriminate between two or more hypotheses on the basis of the observed experimental data. This problem in statistics is known as hypothesis test, and methods have been developed to assign an observation, which consists of the measurements of specific discriminating variables, to one of two or more hypothetical models, considering the predicted probability distributions of the observed quantities under the different possible assumptions.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. R.A. Fisher, The use of multiple measurements in taxonomic problems. Ann. Eugen. 7, 179–188 (1936)

    Article  Google Scholar 

  2. J. Neyman, E. Pearson, On the problem of the most efficient tests of statistical hypotheses. Philos. Trans. R. Soc. Lond. Ser. A 231, 289–337 (1933)

    Article  ADS  Google Scholar 

  3. C. Peterson, T.S. Rgnvaldsson, An introduction to artificial neural networks. LU-TP-91-23. LUTP-91-23, 1991. 14th CERN School of Computing, Ystad, Sweden, 23 Aug–2 Sep 1991

    Google Scholar 

  4. B.P. Roe, H.-J. Yang, J. Zhu, Y. Liu, I. Stancu, G. McGregor, Boosted decision trees as an alternative to artificial neural networks for particle identification. Nucl. Instrum. Methods A543, 577–584 (2005)

    Article  ADS  Google Scholar 

  5. A. Kolmogorov, Sulla determinazione empirica di una legge di distribuzione. G. Ist. Ital. Attuari 4, 83–91 (1933)

    MATH  Google Scholar 

  6. N. Smirnov, Table for estimating the goodness of fit of empirical distributions. Ann. Math. Stat. 19, 279–281 (1948)

    Article  MATH  Google Scholar 

  7. I.M. Chakravarti, R.G. Laha, J. Roy, Handbook of Methods of Applied Statistics, vol. I (Wiley, New York, 1967)

    MATH  Google Scholar 

  8. G. Marsaglia, W.W. Tsang, J. Wang, Evaluating Kolmogorov’s distribution. J. Stat. Softw. 8, 1–4 (2003)

    Google Scholar 

  9. R. Brun, F. Rademakers, Root—an object oriented data analysis framework, in Proceedings AIHENP’96 Workshop, Lausanne, Sep. 1996, Nuclear Instruments and Methods, vol. A389 (1997), pp. 81–86. See also http://root.cern.ch/

  10. M.A. Stephens, EDF statistics for goodness of fit and some comparisons. J. Am. Stat. Assoc. 69, 730–737 (1974)

    Article  Google Scholar 

  11. T.W. Anderson, D.A. Darling, Asymptotic theory of certain “goodness-of-fit” criteria based on stochastic processes. Ann. Math. Stat. 23, 193–212 (1952)

    Article  MathSciNet  MATH  Google Scholar 

  12. H. Cramér, On the composition of elementary errors. Scand. Actuar. J. 1928(1), 13–74 (1928). doi:10.1080/03461238.1928.10416862

    Article  Google Scholar 

  13. R.E. von Mises, Wahrscheinlichkeit, Statistik und Wahrheit (Julius Springer, Vienna, 1928)

    Book  MATH  Google Scholar 

  14. S. Wilks, The large-sample distribution of the likelihood ratio for testing composite hypotheses. Ann. Math. Stat. 9, 60–62 (1938)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. INFN Sezione di Napoli, Napoli, Italy

    Luca Lista

Authors
  1. Luca Lista
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Lista, L. (2016). Hypothesis Tests. In: Statistical Methods for Data Analysis in Particle Physics. Lecture Notes in Physics, vol 909. Springer, Cham. https://doi.org/10.1007/978-3-319-20176-4_7

Download citation

Publish with us

Policies and ethics

Search

Navigation