The null hypothesis significance test and the dichotomization of the p-value: Errare Humanum Est.

Edward Mezones-Holgu��n, Ali Al-Kassab-C��rdova, Percy Soto-Becerra, Sonia Hern��ndez-D��az, Jay S Kaufman
Author Information
  1. Edward Mezones-Holgu��n: Centro de Excelencia en Investigaciones Econ��micas y Sociales en Salud, Universidad San Ignacio de Loyola, Lima, Per��. ORCID
  2. Ali Al-Kassab-C��rdova: Centro de Excelencia en Investigaciones Econ��micas y Sociales en Salud, Universidad San Ignacio de Loyola, Lima, Per��. ORCID
  3. Percy Soto-Becerra: Vicerrectorado de Investigaci��n, Universidad Continental, Huancayo, Per��. ORCID
  4. Sonia Hern��ndez-D��az: Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, EE.UU. ORCID
  5. Jay S Kaufman: Department of Epidemiology, Biostatistics, & Occupational Health, McGill University, Montreal, Canada. ORCID

Abstract

Decision-making in healthcare is complex and needs to be based on the best scientific evidence. In this process, information derived from statistical analysis of data is crucial, which can be developed from either frequentist or Bayesian perspectives. When it comes to the frequentist field, the null hypothesis significance test (NHST) and its p-value is one of the most widely used techniques in different disciplines. However, NHST has been subjected to questioning from different academic points of view, which has led to it being considered as one of the causes of the so-called replicability crisis in science. In this review article, we provide a brief historical account of its development, summarize the underlying methods, describe some controversies and limitations, address misuse and misinterpretation, and finally give some scopes and reflections in the context of biomedical research.

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MeSH Term

Humans
Biomedical Research
Data Interpretation, Statistical
Bayes Theorem

Word Cloud

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