title: The large sample size fallacy.
abstract: BACKGROUND
  Significance in the statistical sense has little to do with significance in the common practical sense. Statistical significance is a necessary but not a sufficient condition for practical significance. Hence, results that are extremely statistically significant may be highly nonsignificant in practice. The degree of practical significance is generally determined by the size of the observed effect, not the p-value. The results of studies based on large samples are often characterized by extreme statistical significance despite small or even trivial effect sizes. Interpreting such results as significant in practice without further analysis is referred to as the large sample size fallacy in this article.
  
  
  OBJECTIVE
  The aim of this article is to explore the relevance of the large sample size fallacy in contemporary nursing research.
  
  
  RESULTS
  Relatively few nursing articles display explicit measures of observed effect sizes or include a qualitative discussion of observed effect sizes. Statistical significance is often treated as an end in itself.
  
  
  CONCLUSIONS
  Effect sizes should generally be calculated and presented along with p-values for statistically significant results, and observed effect sizes should be discussed qualitatively through direct and explicit comparisons with the effects in related literature.
authors.affiliation: University of Borås, Borås, Sweden. bjorn.lantz@hb.se
authors.forename: Björn
authors.initials: B
authors.lastname: Lantz
mesh_terms: D006801:Humans; D015399:Nursing Research; D018401:Sample Size