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Objectives: To (a) assess the statistical power of nursing research to detect small, medium, and large effect sizes; (b) estimate the experiment-wise Type I error rate in these studies; and (c) assess the extent to which (i) a priori power analyses, (ii) effect sizes (and interpretations thereof), and (iii) confidence intervals were reported. Design: Statistical review. Data sources: Papers published in the 2011 volumes of the 10 highest ranked nursing journals, based on their 5-year impact factors. Review methods: Papers were assessed for statistical power, control of experiment-wise Type I error, reporting of a priori power analyses, reporting and interpretation of effect sizes, and reporting of confidence intervals. The analyses were based on 333 papers, from which 10,337 inferential statistics were identified. Results: The median power to detect small, medium, and large effect sizes was .40 (interquartile range [IQR] = .24-.71), .98 (IQR = .85-1.00), and 1.00 (IQR = 1.00-1.00), respectively. The median experiment-wise Type I error rate was .54 (IQR = .26-.80). A priori power analyses were reported in 28% of papers. Effect sizes were routinely reported for Spearman's rank correlations (100% of papers in which this test was used), Poisson regressions (100%), odds ratios (100%), Kendall's tau correlations (100%), Pearson's correlations (99%), logistic regressions (98%), structural equation modelling/confirmatory factor analyses/path analyses (97%), and linear regressions (83%), but were reported less often for two-proportion z tests (50%), analyses of variance/analyses of covariance/multivariate analyses of variance (18%), t tests (8%), Wilcoxon's tests (8%), Chi-squared tests (8%), and Fisher's exact tests (7%), and not reported for sign tests, Friedman's tests, McNemar's tests, multi-level models, and Kruskal-Wallis tests. Effect sizes were infrequently interpreted. Confidence intervals were reported in 28% of papers. Conclusion: The use, reporting, and interpretation of inferential statistics in nursing research need substantial improvement. Most importantly, researchers should abandon the misleading practice of interpreting the results from inferential tests based solely on whether they are statistically significant (or not) and, instead, focus on reporting and interpreting effect sizes, confidence intervals, and significance levels. Nursing researchers also need to conduct and report a priori power analyses, and to address the issue of Type I experiment-wise error inflation in their studies. [ABSTRACT FROM AUTHOR]

Background Venous leg ulcer management in the UK varies significantly. Judgements made by nurses contribute to this variability and it is often assumed that specialist nurses make better judgements than non-specialist nurses. This paper compares the judgements of community tissue viability specialist nurses and community generalist nurses; specifically, the ways they use clinical information and their levels of accuracy. Objectives To compare specialist and non-specialist UK community nurses’ clinical information use when managing venous leg ulceration and their levels of accuracy when making diagnoses and judging the need for treatment. Design Judgement analysis. Setting UK community and primary care nursing services. Participants 18 community generalist nurses working in district (home) nursing teams and general practitioner services and 18 community tissue viability specialist nurses. Methods Data were collected in 2011 and 2012. 18 community generalist nurses and 18 community tissue viability specialist nurses made diagnostic and treatment judgements on 110 clinical scenarios and indicated their confidence in each of their judgements. Scenarios were generated from real patient cases and presented online using text and photographs. An expert panel made judgements, and reached consensus on the same scenarios. These judgements were used as a standard against which to compare the participants. Logistic regression models and correlational statistics were used to generate various indices of judgement “performance”: accuracy, consistency, confidence calibration and information use. Differences between groups of nurses with different levels of characteristics linked to expertise were explored using analysis of variance. Results Specialist nurses had similar cue usage to the generalist nurses but were more accurate when making diagnostic and treatment judgements. Conclusion It is not obvious why the tissue viability specialist nurses were more accurate. One possible reason might be the greater opportunities for ‘deliberate practice’ afforded to specialists. However, restricting aspects of practice only to specialist nurses is likely to hinder the judgement performance of generalists. [ABSTRACT FROM AUTHOR]