NR 505 Week 6: Statistical and Clinical Significance

NR 505 Week 6: Statistical and Clinical Significance

NR 505 Week 6: Statistical and Clinical Significance

Statistical and clinical significance of research are both very important within the medical community and both are only productive when they bring change or influence on the patient population. According to El-Masri (2016) a statistically significant result shows the observed effect is not likely due to chance. For example, the values assigned to rule out the null hypothesis are based on pre-determined criteria. Statistical significance allows the researcher to make determinations based on the study findings and data as to the true value this research will have on the patient population. Clinical significance measures the magnitude of the relationship between the independent variable and the outcome variable (El-Masri, 2016). Clinical significance is measured by the value it brings to quality of life, patient outcomes, and cost. Healthcare professionals must be careful to ensure the results of the study are credible and they can be transferred to positively impact a patient population.

            In my opinion, I believe you can accept the null hypothesis and still demonstrate clinical significance. My understanding of clinical significance is the impact it brings to the patient population from the research data available whether the results show productive change or harmful change. “Was the treatment plan effective?” and “Does it have significance within the clinical practice?” are questions that help determine clinical significance. For example, a hypothetical research study on a group of clinically depressed patients utilizing cognitive behavior therapy as the independent variable showed no statistically significant difference between the control group and the independent group than the null hypothesis is accepted. However, the clinical results of the data showed many participants responded well to cognitive behavior therapy and verbalized improved mood, outlook, and quality of life. The researcher could look at confidence intervals to determine if cognitive behavior therapy had clinical significance on the depressed patient population and could produce change within the clinical setting such as improved quality care outcomes, patient satisfaction, or a reduction in hospital stays. Clinical significance refers to the real-life impact of research findings (El-Masri, 2016).

            According to Connelly (2014) the clinical significance of a study must be determined by the clinician because they know the needs of their patient population and the practice setting in which they operate. In my opinion, if I questioned the credibility of the qualitative study I could still potentially find clinical significance in my practice area. For example, a hypothetical study on veterans’ suicide risk post deployment with in-depth interviews on a sample size of approximately 20 veterans to discuss their experience with re-integration into society and the effects on their mental health needs. The credibility of the data could be in question simply because the saturation of the data did not occur. However, as a clinician working in a rural setting with a large veteran population, I see how certain questions regarding post deployment and mental health needs could improve the quality of life and possibly reduce the suicide risk within my patient population then I have discovered clinical significance while questioning the credibility of the study.

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Connelly, L.M. (2014). Statistical and clinical significance. Medsurg Nursing: Official Journal of the Academy of Medical-Surgical Nurses, 23(2), 118-9.

El-Masri, M. M. (2016). Statistical versus clinical significance in nursing research. The Canadian Journal of Nursing Research = Revue Canadienne De Recherche en Sciences Infirmieres48(2), 31-32. doi:10.1177/0844562116677895

            The question I chose this week discusses how my research on veterans could be transferred to a different population group. I found this question fascinating and I feel I have two distinct population groups that could fall into a similar situation as veterans returning from combat and reintegrating back into a civilian lifestyle. The first group would be police officers who are accustomed to putting their life on the line every day in the line of duty. I can see where police officers see many fellow officers wounded or killed because of protecting others. Meffert (2014) discussed the similarities between a combat veteran and a police officer and the level of stress endured and the high rate of post traumatic stress disorder (PTSD) as a result of their career choice. Police officers are often the first line of defense and first responder in a life-threatening situation, emergency scenario, or domestic abuse where women and children are being assaulted.

            The qualitative research on post war veterans could easily relate to police officers as they try to have normal lives and when they are off duty. The common thread with these two disciplines is the PTSD from work related violence that permeates their existence on and off duty. Meffert (2014) discusses how PTSD can impact work and personal relationships and effect overall mental health and the individual’s ability to relate to others. The second group I feel could relate to the qualitative study would be victims of domestic violence. In this population group there is a combat zone within the home setting thus increasing the risks for victims to experience PTSD. Victims of domestic abuse can have a difficult time relating to other individuals within the community and can feel isolated and alone and live in a constant posture of defense and fear for their personal safety. This posture of defense can impact mental well-being and a sense of belonginess similar to that of the post war veteran. All three situations could benefit from qualitative research on reintegration into community following life outside of their previous combat zone.

Meffert, S. M., Henn-Haase, C., Metzler, T. J., Qian, M., Best, S., Hirschfeld, A., & Marmar, C. R. (2014). Prospective study of police officer spouse/partners: a new pathway to secondary trauma and relationship violence. Plos One9(7), e100663. doi:10.1371/journal.pone.0100663

Researchers spend many months to years developing their projects, conducting the study, and analyzing the results.  Also, if there is one example of a theory being incorrect, then that proves the theory is either void or not complete (H.C. van Rijn, Bech, Bouyer, J.G. van den Brand, 2017).  Researchers write about the importance of both statistical and clinical relevance of results.  Statistically significant results help to prove or disprove there may be a correlation between two or more factors. However; just because a study is statistically significant does not mean it is clinically relevant.  When we speak about statistical significance we must think about the sample size.  Sample size is the total number of participants in the study.  If the study is small in size, then statistically is the data analyzed important? With smaller sample size, then clinical error becomes more probable.  As well during a research study, the researchers are attempting to find clinical significance which is the real-world application of the research conducted.  Specifically, is the new therapy good enough to drop the old therapy?  We see this in our practice quite often.  When I first started nursing, it was very important for patients diagnosed with a DVT to be on complete bedrest then on a heparin drip, and possibly undergo an IVC filter placement.  Now, those therapies are not as clinically appropriate.  Patients with DVT’s are allowed to ambulate and IVC filter placement is not commonly used anymore, due to problems with their placement and retrieval. With a null hypothesis, there is no significant difference between the two populations being studied.  This could be due to an experimental or sampling error during the study.  An example of this would be in my EBP.  I am hypothesizing with the use of a dedicated education unit, new graduate nurses will be more prepared to care for patients therefore retention will be higher, compared to the traditional 1:1 method of precepting.  Now, after all my research and study is complete, and I find there is actually no difference between these two groups, then the null hypothesis would be correct.  I could have made some error during my initial planning of my project or study itself. This could be very clinically significant.  Because for the hospital, they may not be willing to create a unit dedicated to education for the new graduate.  In the case of qualitative studies.  These research projects are more observational than statistical.  As well, the researchers are looking into concepts or characteristics of a phenomenon.  In my practice area, a qualitative study would be important. Working in a cath lab environment, I could research the stressors on the staff related to being on call for STEMI’s. These stressors could include sleeplessness and anxiety.  When we are on call and a STEMI occurs in the middle of the night, we are still expected to be at work and functional for the next day. This can lead to unhappiness with their jobs and terminating their employment. 




H.C. van Rijn, M., Bech, A., Bouyer, J., & J.G. van den Brand, A.  (2017). Statistical significance versus clinical relevance.  Nephrology Dialysis Transplantation, 32, 6-12. Retrieved from: to an external site.