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Revista Mexicana de Cardiología

ISSN 0188-2198 (Print)
En 2019, la Revista Mexicana de Cardiología cambió a Cardiovascular and Metabolic Science

Ver Cardiovascular and Metabolic Science


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2015, Number 1

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Rev Mex Cardiol 2015; 26 (1)

Demystification of the significance of p in statistical tests

Sánchez TRA
Full text How to cite this article

Language: English
References: 11
Page: 56-58
PDF size: 133.27 Kb.


Key words:

Statistic test, p value, p significance.

ABSTRACT

All statistical tests have a p value that is significant when ‹ 0.050. This value was arbitrarily determined by RA Fisher and accepted consensually over time. Since its genesis, this value has been questioned, and nowadays it is under the careful eye of many statisticians. This issue has led to a debate among the scientific community: obtaining p significance was considered as a guarantee that the research project would be an appropriate contrast between the hypothesis and the acceptance, or rejection, of it. The purpose of this paper is to construct a discussion about p significance.


REFERENCES

  1. Fisher RA. Statistical methods for research workers. 5 de Biological monographs and manuals. La Universidad de California, 1932, pp. 1-307.

  2. Lew MJ. To P or not to P: on the evidential nature of P-values and their place in scientific inference stat. ME. 2013; 3: 27-46.

  3. Nuzzo R. P values, the “gold standard” of statistical validity, are not as reliable as many scientists assume. Nature. 2014; 506: 150-152.

  4. Donna M, Windish MD, Stephen J, Huot SJ, Green ML. Medicine residents’ understanding of the biostatistics and results in the medical literature. JAMA. 2007; 298: 1010-1022.

  5. Lecoutre MP, Poitevineau J, Lecoutre B. Even statisticians are not immune to misinterpretations of null hypothesis significance tests. Int J Psychol. 2003; 38: 37-45.

  6. Hubbard R, Lindsay MR. Why p values are not a useful measure of evidence in statistical significance testing. Theory Psychol. 2008; 18: 69-88.

  7. Lew MJ. Bad statistical practice in pharmacology (and other basic biomedical disciplines): you probably don’t know P. Br J Pharmacol. 2012; 166: 1559-1567.

  8. Goodman S. A dirty dozen: twelve p-value misconceptions. Semin Hematol. 2008; 45: 135-140.

  9. Hurlbert SH, Lombardi CM. Final collapse of the Neyman-Pearson decision theoretic framework and rise of the neo Fisherian. Ann Zool Fennici. 2009; 46: 311-349.

  10. Coe R. It’s the effect size, stupid. What effect size is and why it is important. School of education, University of Durham. Paper presented at the British Educational Research Association, Annual Conference. Exter, 12-14 September, 2002.

  11. Cohen J. Statistical power analysis for the behavioral sciences. New York, Academic Press. 1977, pp. 216-380.




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Rev Mex Cardiol. 2015;26