Skip to main content
Log in

The researcher and the consultant: a dialogue on null hypothesis significance testing

  • DIALOGUE
  • Published:
European Journal of Epidemiology Aims and scope Submit manuscript

An Erratum to this article was published on 13 March 2014

Abstract

Since its introduction, null hypothesis significance testing (NHST) has caused much debate. Many publications on common misunderstandings have appeared. Despite the many cautions, NHST remains one of the most prevalent, misused and abused statistical procedures in the biomedical literature. This article is directed at practicing researchers with limited statistical background who are driven by subject matter questions and have empirical data to be analyzed. We use a dialogue as in ancient Greek literature for didactic purposes. We illustrate several, though only a few, irritations that can come up when a researcher with minimal statistical background but a good sense of what she wants her study to do, and of what she wants to do with her study, asks for consultation by a statistician. We provide insights into the meaning of several concepts including null and alternative hypothesis, one- and two-sided null hypotheses, statistical models, test statistic, rejection and acceptance regions, type I and II error, p value, and the frequentist’ concept of endless study repetitions.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Anderson DR, Burnham KP, Thompson WL. Null hypothesis testing: problems, prevalence, and an alternative. J Wildl Manag. 2000;64:912–23.

    Article  Google Scholar 

  2. Baldick C. Oxford dictionary of literary terms. Oxford: Oxford University Press; 2008.

    Book  Google Scholar 

  3. Box GEP. Sampling and Bayes’ inference in scientific modelling and robustness. J R Stat Soc A. 1980;143:383–430.

    Article  Google Scholar 

  4. Cox DR. The role of significance tests. Scand J Stat. 1977;4:49–70.

    Google Scholar 

  5. Fisher RA. Statistical methods for research workers. Edingburgh: Oliver and Boyd; 1925.

    Google Scholar 

  6. Fisher RA. Statistical methods and scientific inference. Edingburgh: Oliver and Boyd; 1956.

    Google Scholar 

  7. Gigerenzer G, Swijtink Z, Porter T, et al. The empire of chance. how probability changed science and everyday life. Cambridge: Cambridge University Press; 1989.

    Book  Google Scholar 

  8. Goodman S. A dirty dozen: twelve p-value misconceptions. Semin Hematol. 2008;45:135–40.

    Article  PubMed  Google Scholar 

  9. Goodman SN. P values, hypothesis tests, and likelihood: implications for epidemiology of a neglected historical debate. Am J Epidemiol. 1993;137:485–96.

    CAS  PubMed  Google Scholar 

  10. Greenland S. Multiple-bias modelling for analysis of observational data. J R Stat Soc A. 2005;168:267–306.

    Article  Google Scholar 

  11. Greenland S. Null misinterpretation in statistical testing and its impact on health risk assessment. Prev Med. 2011;53:225–8.

    Article  PubMed  Google Scholar 

  12. Greenland S. Nonsignificance plus high power does not imply support for the null over the alternative. Ann Epidemiol. 2012;22:364–8.

    Article  PubMed  Google Scholar 

  13. Greenland S, Poole C. Problems in common interpretations of statistics in scientific articles, expert reports, and testimony. Jurimetrics. 2011;51:129.

    Google Scholar 

  14. Hubbard R. Alphabet soup: blurring the distinction between p’s and α’s in psychological research. Theory Psychol. 2004;14:295–327.

    Article  Google Scholar 

  15. Hubbard R, Bayarri MJ. Confusion over measures of evidence (p’s) versus errors (α’s) in classical statistical testing (with discussion). Am Stat. 2003;57:171–82.

    Article  Google Scholar 

  16. Jeffreys H. Theory of probability. Oxford: Clarendon Press; 1939.

    Google Scholar 

  17. Kirk RE. Statistical consulting in a University: dealing with people and other challenges. Am Stat. 1991;45:28–34.

    Google Scholar 

  18. Leamer EE. Specification searches. New York: Wiley; 1978.

    Google Scholar 

  19. Neyman J. Frequentist probability and frequentist statistics. Synthese. 1977;36:97–131.

    Article  Google Scholar 

  20. Neyman J, Pearson ES. On the use and interpretation of certain test criteria for purposes of statistical inference: Part I. Biometrika. 1928;20A:175–240.

    Google Scholar 

  21. Neyman J, Pearson ES. The testing of statistical hypotheses in relation to probabilities a priori. Proc Cambridge Philos Soc. 1933;29:492–510.

    Article  Google Scholar 

  22. Pocock SJ, Ware JH. Translating statistical findings into plain English. Lancet. 2009;373:1926–8.

    Article  PubMed  Google Scholar 

  23. Robins JM, Greenland S. The role of model selection in causal inference from nonexperimental data. Am J Epidemiol. 1986;123:392–402.

    CAS  PubMed  Google Scholar 

  24. Rothman KJ, Greenland S, Lash TL. Precision and validity in epidemiologic studies. In: Rothman KJ, Greenland S, Lash TL, editors. Modern epidemiology. Philadelphia: Wolters Kluwer, Lippincott Williams and Wilkins; 2008. p. 148–67.

    Google Scholar 

  25. Section on Statistical Consulting.American Statistical Association. When you consult a statistician… what to expect. 2003.

  26. Stegman CE. Statistical consulting in the university: a faculty member’s perspective. J Educ Stat. 1985;10:269–82.

    Article  Google Scholar 

  27. Tukey JW. Unsolved problems of experimental statistics. J Am Stat Assoc. 1954;49:706–31.

    Google Scholar 

Download references

Acknowledgments

We would like to thank Sander Greenland PhD, Department of Epidemiology & Department of Statistics, University of California, Los Angeles, for helpful comments and suggestions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andreas Stang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Stang, A., Poole, C. The researcher and the consultant: a dialogue on null hypothesis significance testing. Eur J Epidemiol 28, 939–944 (2013). https://doi.org/10.1007/s10654-013-9861-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10654-013-9861-4

Keywords

Navigation