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.