Sample Size Justification in Quantitative Research

Sample-size justification remains a central element of quantitative research, yet classical a priori power analysis is frequently applied to designs for which it is neither required nor conceptually appropriate. When power calculations are used indiscriminately, they may misalign with the aims of studies that do not need them. This poses challenges for researchers and student nurses, particularly those without specialist training in statistics, and can lead to methodological inconsistencies across the published nursing literature.

In this paper, I aim to clarify how sample-size justification can be aligned with study purpose and to describe design-appropriate alternatives to traditional hypothesis-testing power analysis in common, non-inferential nursing studies.

This conceptual discussion of the methodological and statistical literature examines the assumptions underlying classical power analysis and identifies study designs in which these assumptions may not hold. The discussion groups commonly use quantitative designs by primary purpose and presents alternative justification frameworks.

Part A illustrates common research contexts in which traditional power analysis may not be appropriate and presents the methodological rationale for alternative approaches to sample-size justification. In Part B, alternative approaches are presented, such as confidence-interval precision planning and detectable-effect reporting that offer more coherent and transparent approaches to sample-size reasoning. A summary table provides practical sample-size calculation guide with examples. Part C contains a practice experience reflection.

Aligning sample-size justification with the methodological purpose of the study strengthens transparency, improves conceptual coherence, and enhances ethical accountability. Misapplication of power analysis can lead to unstable effect-size estimates, misinterpretation, and inefficient use of research resources. A design-specific approach can therefore help maintain methodological rigor across quantitative nursing research.