  Nurses should rely on the best evidence, but tend to struggle with
statistics, impeding research integration into clinical practice. Statistical
significance, a key concept in classical statistics, and its primary metric,
the p-value, are frequently misused. This topic has been debated in many
disciplines but rarely in nursing. The aim is to present key arguments in the
debate surrounding the misuse of p-values, discuss their relevance to nursing,
and offer recommendations to address them. The literature indicates that the
concept of probability in classical statistics is not easily understood,
leading to misinterpretations of statistical significance. Much of the critique
concerning p-values arises from such misunderstandings and imprecise
terminology. Thus, some scholars have argued for the complete abandonment of
p-values. Instead of discarding p-values, this article provides a comprehensive
account of their historical context and the information they convey. This will
clarify why they are widely used yet often misunderstood. The article also
offers recommendations for accurate interpretation of statistical significance
by incorporating other key metrics. To mitigate publication bias resulting from
p-value misuse, pre-registering the analysis plan is recommended. The article
also explores alternative approaches, particularly Bayes factors, as they may
resolve several of these issues. P-values serve a purpose in nursing research
as an initial safeguard against the influence of randomness. Much criticism
directed towards p-values arises from misunderstandings and inaccurate
terminology. Several considerations and measures are recommended, some which go
beyond the conventional, to obtain accurate p-values and to better understand
statistical significance. Nurse educators and researchers should considerer
these in their educational and research reporting practices.
