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Hypothesis testing and p values: how to interpret results and reach the right conclusions
  1. Allison Shorten,
  2. Brett Shorten
  1. Yale University School of Nursing, New Haven, Connecticut, USA
  1. Correspondence to: Dr Allison Shorten
    Yale University School of Nursing, 100 Church Street South, PO Box 9740, New Haven, CT 06536 USA; allison.shorten{at}yale.edu

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Whenever we encounter a research finding based on the interpretation of a p value from a statistical test, whether we realise it or not, we are discussing the result of a formal hypothesis test. This is true irrespective of whether the test involves comparisons of means, Odds Ratios (ORs), regression results or other types of statistical tests. As readers of research, it is important to understand the underlying principles of hypothesis testing, so that when faced with statistical results, we reach the right conclusions and make good decisions about which findings are robust enough to be translated into clinical practice.

The article by Yinon et al1 featured in a recent EBN commentary, will be used to illustrate four simple steps involved in hypothesis testing.2 The authors of this paper explored the possible benefits of antenatal steroid administration in the context of late preterm birth (>34 weeks gestation). One of the key outcomes of interest included the incidence of babies being admitted to a special care unit …

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