Skip to main content
SpringerLink
Log in

Do We Understand Classic Statistics?

  • Chapter
  • First Online:
Bayesian Data Analysis for Animal Scientists

Abstract

In this chapter, we review the classical statistical concepts and procedures, test of hypothesis, standard errors and confidence intervals, unbiased estimators, maximum likelihood, etc., and we examine the most common misunderstandings about them. We will see the limitations of classical statistics in order to stress the advantages of using Bayesian procedures in the following chapters.

Without hoping to know whether each separate hypothesis is true or false, we may search for rules to govern our behaviour with regard to them, in following which we insure that, in the long run of experience, we shall not be too often wrong.

Jerzy Neyman and Egon Pearson, 1933

The original version of this chapter was revised. A correction to this chapter is available at https://doi.org/10.1007/978-3-319-54274-4_11.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Change history

  • 27 November 2018

    This book was inadvertently published with wrong details in Chapters 1, 4, 8, 10, and Appendix. The original book has been updated accordingly.

References

  • Barnett V (1999) Comparative statistical inference. Wiley, Chichester

    Book  Google Scholar 

  • Bayarri MJ, Berger JO (2004) The interplay of Bayesian and frequentist analysis. Stat Sci 19:58–80

    Article  Google Scholar 

  • Bellhouse DR (2004) The Reverend Thomas Bayes, FRS: a biography to celebrate the tercentenary of his birth. Stat Sci 19:3–43

    Article  Google Scholar 

  • Berger JO, Sellke T (1987) Testing a point null hypothesis: the irreconcilability of P-values and evidence. J Amer Stat Assoc 82:112–122

    Google Scholar 

  • Berger JO, Wolpert RL (1984) The likelihood principle, Institute of Mathematical Statistics. Lecture Notes—Monograph Series. Purdue University

    Google Scholar 

  • Berkson J (1938) Some difficulties of interpretation encountered in the application of the chi-squaretest. J Am Stat Assoc 33:526–542

    Article  Google Scholar 

  • Blasco A (2001) The Bayesian controversy in animal breeding. J Anim Sci 79:2023–2046

    Article  CAS  Google Scholar 

  • Childers T (2013) Philosophy and probability. Oxford University Press, Oxford

    Google Scholar 

  • de Finetti B (1937) La prévision: ses lois logiques, ses sources subjectives. Annales de l’Institut Henri Poincairé 7:1–68. Translated in Kyburg HE, Smokler HE (1964) Studies in subjective probability. Wiley, New York

    Google Scholar 

  • Efron B, Hastie T (2016) Computer age statistical inference. Cambridge University Press, New York

    Book  Google Scholar 

  • Fienberg SE (2006) When did Bayesian inference become “Bayesian”? Bayesian Anal 1:1–40

    Article  Google Scholar 

  • Fisher RA (1912) On an absolute criterion for fitting frequency curves. Messenger Math 41:155–160. Reprinted in Stat Sci 1997, 12:39–41

    Google Scholar 

  • Fisher R (1921) On the “probable error” of a coefficient of correlation deduced from a small sample. Metron 1:3–32

    Google Scholar 

  • Fisher R (1922) On the mathematical foundations of theoretical statistics. Phil Trans A 222:309–368

    Article  Google Scholar 

  • Fisher R (1925) Statistical methods for research workers. Oliver and Boyd, Edinburgh

    Google Scholar 

  • Fisher R (1935) The logic of inductive inference. J R Stat Soc 98:39–82

    Article  Google Scholar 

  • Fisher R (1950) Contributions to mathematical statistics. Wiley, New York

    Google Scholar 

  • Fisher R (1959) Statistical methods and scientific inference, 2nd edn. Oliver and Boyd, Edinburgh

    Google Scholar 

  • Gauss CF (1809) Theoria motus corporum coelestium in sectionibus conicis solem ambientium (trans: Davis CE, 1963). Dover, New York

    Google Scholar 

  • Gelfand AE, Smith FM (1990) Sampling-based approaches to calculating marginal densities. J Am Stat Assoc 85:398–409

    Article  Google Scholar 

  • Geman S, Geman D (1986) Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 6:721–742

    Google Scholar 

  • Goodman SN (1993) p-values, hypothesis tests and likelihood: implications for epidemiology of a neglected historical debate. Am J Epidemiol 137:485–496

    Article  CAS  Google Scholar 

  • Hald A (1998) A history of mathematical statistics from 1750 to 1930. Wiley, New York

    Google Scholar 

  • Henderson CR (1973) Sire evaluation and genetic trends. In: Proc. Anim. Breed. and Genet. Symp. in honor of Dr. J. L. Lush. Blacksburg, Virginia, pp 10–41

    Article  Google Scholar 

  • Howie D (2002) Interpreting probability: controversies and developments in the early twentieth century. Cambridge University Press, New York

    Book  Google Scholar 

  • Jeffreys H (1939/1961). Theory of probabilities, 1st and 3rd edn. Clarendon Press, Oxford

    Google Scholar 

  • Johnson DH (1999) The insignificance of statistical significance testing. J Wildl Manag 63:763–772

    Article  Google Scholar 

  • Johnson VE (2013) Revised standards for statistical evidence. Proc Natl Acad Sci 110:9313–19317

    Google Scholar 

  • Kendall MG (1961) Daniel Bernoulli on maximum likelihood. Biometrika 48:1–8

    Article  Google Scholar 

  • Kendall M, Stuart A, Ord K (1994) Kendall’s advanced theory of statistics, volume 1: distribution theory. Arnold, London

    Google Scholar 

  • Lecoutre B, Poitevineau J (2014) The significance test controversy revisited. Springer, New York

    Google Scholar 

  • Lundgren H, Canario L, Grandinson K, Lundeheim N, Zumbach B, Vangen O, Rydhmer L (2010) Genetic analysis of reproductive performance in Landrace sows and its correlation to piglet growth. Livest Sci 128:173–178

    Article  Google Scholar 

  • McGrayne SB (2011) The theory that would not die. Yale University Press, New Haven, NJ

    Google Scholar 

  • Metropolis N (1987) The beginning of the Monte Carlo method. Los Alamos Sci 15(Special Issue):125–130

    Google Scholar 

  • Metropolis N, Ulam S (1949) The Monte Carlo method. J Am Stat Assoc 44:335–341

    Article  CAS  Google Scholar 

  • Mill JS (1848) On liberty. Reprinted in 2006 by Longman

    Google Scholar 

  • Neyman J (1977) Frequentist probability and frequentist statistics. Synthese 36:97–131

    Article  Google Scholar 

  • Neyman J, Pearson E (1933) On the problem of the most efficient test of statistical hypotheses. Phil Trans R Soc 231A:289–337

    Article  Google Scholar 

  • Popper K (1934) Logik der Forschung. Springer

    Google Scholar 

  • Ramsey FP (1931) Truth and probability. In: Braithwaite RB (ed) The foundation of mathematics and other logical essays. Routledge & Kegan, London

    Google Scholar 

  • Robinson GK (1991) That BLUP is a good thing: the estimation of random effects. Stat Sci 6:15–51

    Article  Google Scholar 

  • Sellke T, Bayarri MJ, Berger JO (2001) Calibration of P-values for testing precise null hypotheses. Am Stat 55:62–71

    Article  Google Scholar 

  • Sorensen DA, Wang CS, Jensen J, Gianola D (1994) Bayesian analysis of genetic change due to selection using Gibbs sampling. Genet Sel Evol 26:333–360

    Article  Google Scholar 

  • Stigler SM (1983) Who discovered Bayes theorem? Am Stat 37:290–296

    Google Scholar 

  • Stigler SM (2013) The true title of Bayes’s essay. Stat Sci 28(3):283–288

    Article  Google Scholar 

  • Student (1908) On the probable error of a mean. Biometrika 6:1–25

    Article  Google Scholar 

  • Tribout T, Caritez JC, Gruand J, Bouffaud M, Guillouet P, Billon Y, Péry C, Laville E, Bidanel JP (2010) Estimation of genetic trends in French Large White pigs from 1977 to 1998 for growth and carcass traits using frozen semen. J Anim Sci 88:2856–2867

    Article  CAS  Google Scholar 

  • Visscher PM (1998) On the sampling variance of intraclass correlations and genetic correlations. Genetics 149:1605–1614

    CAS  PubMed  PubMed Central  Google Scholar 

  • Von Mises R (1928/1957) Probability statistics and truth. Macmillan Publ. Co., London

    Google Scholar 

  • Wang CS, Gianola D, Sorensen DA, Jensen J, Christensen A, Rutledge JJ (1994) Response to selection for litter size in Danish Landrace pigs: a Bayesian analysis. Theor Appl Genet 88:220–230

    CAS  PubMed  Google Scholar 

  • Yates F (1990) Preface of statistical methods, experimental design, and scientific inference by R.A. Fisher. Oxford University Press, Oxford

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Animal Science and Technology, Universitat Politècnica de València, València, Spain

    Agustín Blasco

Authors
  1. Agustín Blasco
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Blasco, A. (2017). Do We Understand Classic Statistics?. In: Bayesian Data Analysis for Animal Scientists. Springer, Cham. https://doi.org/10.1007/978-3-319-54274-4_1

Download citation

Publish with us

Policies and ethics

Search

Navigation