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Nurses should rely on the best evidence, but tend to struggle withstatistics, impeding research integration into clinical practice. Statisticalsignificance, a key concept in classical statistics, and its primary metric,the p-value, are frequently misused. This topic has been debated in manydisciplines but rarely in nursing. The aim is to present key arguments in thedebate surrounding the misuse of p-values, discuss their relevance to nursing,and offer recommendations to address them. The literature indicates that theconcept of probability in classical statistics is not easily understood,leading to misinterpretations of statistical significance. Much of the critiqueconcerning p-values arises from such misunderstandings and impreciseterminology. Thus, some scholars have argued for the complete abandonment ofp-values. Instead of discarding p-values, this article provides a comprehensiveaccount of their historical context and the information they convey. This willclarify why they are widely used yet often misunderstood. The article alsooffers recommendations for accurate interpretation of statistical significanceby incorporating other key metrics. To mitigate publication bias resulting fromp-value misuse, pre-registering the analysis plan is recommended. The articlealso explores alternative approaches, particularly Bayes factors, as they mayresolve several of these issues. P-values serve a purpose in nursing researchas an initial safeguard against the influence of randomness. Much criticismdirected towards p-values arises from misunderstandings and inaccurateterminology. Several considerations and measures are recommended, some which gobeyond the conventional, to obtain accurate p-values and to better understandstatistical significance. Nurse educators and researchers should considererthese in their educational and research reporting practices.

Reexamining Statistical Significance and P-Values in Nursing Research:  Historical Context and Guidance for Interpretation, Alternatives, and  Reporting