Introduction
Statistical significance and clinical importance of results are two critical elements necessary for conducting valid and reliable research in clinical nursing and evidence-based medicine to implement its outcomes into practice. However, a researcher should remember that these two concepts are not identical to each other (Burns & Grove, 2015). Hence, statistical significance and clinical importance have certain similar and different aspects, which are necessary to be identified.
Comparing and Contrasting Concepts
Statistical significance represents statistical methods that allow researchers to estimate the probability of an observed or higher degree of correlation between independent and dependent variables with the validity of the null hypothesis. The achieved level of statistical significance is the probability of the erroneous rejection of the null hypothesis, calculated using sample data. As for clinical importance, this term means a considerable shift in the outcome between the treatment and the control group, which represents a practical impact on the patient. Statistically significant study results in clinical nursing can be interpreted as clinically important only if it has the potential to improve outcomes of medical interventions (Greenland, Senn, Rothman, et al., 2016). However, such results must be properly evaluated before they can be put into practice.
The two concepts are similar because they reflect the quality of the clinical nursing research results, and both indicators are necessary for the study’s reliability. However, it is critical to understand the main difference between them. A statistically significant result is based on the calculation of probabilistic criteria, while a clinically important result is a conclusion that has implications for the treatment and patient care.
Conclusion
The analysis of the concepts of static significance and clinical importance, which are among the main in evidence-based medicine and clinical nursing, demonstrates that, at first glance, they seem to be similar. However, it is not exactly right, because even though they are related, they reflect different sides of research outcomes. Therefore, it is vital to separate these concepts and apply both carefully in the process of conducting clinical nursing research.
References
Burns, N., & Grove, S. (2015). Understanding nursing research: Building an evidence-based practice (6th ed.). Philadelphia, PA: Saunders.
Greenland, S., Senn, S.J., Rothman, K.J. et al. (2016). Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. European Journal of Epidemiology, 31(4), 337-350.