Descriptive Statistics and Clinical Significance, Essay Example

Leaders within the current healthcare system frequently uses statistics in order to render important medical decisions and analyze test results in various departments. Nurse leaders use descriptive statistics to outline various test results procured from a sample often deploying charts and tables, while inferential statistics are utilize to convey the veracity of the results yielded from various samples taken and how they can be appropriated to various populations. Descriptive statistics are routinely used in my practice as a emergency room nurse where I specifically monitor the elderly and ensure that they avoid any hard falls. One way I use descriptive statistics frequent is to calculate various percentages of. Procuring data regarding patient care and gauging conformity to core measures enables healthcare providers to validate the course of medical treatment outlined and administered. Tudor et al. (2014) conducted a study that was descriptive in nature utilizing a cross-sectional survey that included a sample of 154 nurses who worked in both inpatient and outpatient settings in a metropolitan hospital. The survey consisted of 63 items that included demographic questions,  2 scales that were validated prior, and opinion questions. Results showed that the self-confidence of nurses was directly related to the nurse’s perception of family presence during resuscitation.

A confidence interval that is calculated as a measurement of the effects of medical treatment demonstrates the range that the treatment effect is more likely to fall in. It is subject to a vast array of assumptions. It is one way that researchers can help decide if a certain statistical outcome, significant or not, is pertinent to nursing practices. Medical personnel prefer confidence intervals over p-values because they tell clinicians the entire range of possible effect sizes that are accordant with the data. Statistical significance refers to an assertion regarding the probability of medical findings germinated because of chance. Clinical significance refers to testing that is shaped by p-values and yields dichotomous results regardless if they are statistically important or not. Such a limited interpretation of results to only p-values translated into medical researchers to underestimate or overestimate the results yielded (Fetney, 2010). The objective of clinical research is to test out a proposed intervention with the aim of procuring results based on a sample that may be applicable to a much wider population.  P-values do not yield any data regarding the overall meaning or significance of results for the purpose of clinical practices. They also  do not offer any information on what might transpire in the future.  Clinical significance refers to a decision predicated on the relevance or practical currency of a certain treatment, which may or may not utilize statistical significance as a preliminary prerequisite. The majority of physicians and nurses only care about the statistical significance or their primary aims because they only want to ascertain whether the p-value is under alpha. However, it must be noted that the p-value provides very limited data, failing to demonstrate how significant statistical analysis results are. I think clinical significance retains more currency. In the domain of clinical research, it is important for researchers to evaluate the importance of noted differences between the groups being evaluated. Moreover, it is important to measure how yielded outcomes retain currency such as the effectiveness of a proposed intervention. Statistical significance elides such critical data, especially regarding the clinical pertinence or the effect size.

References

Fethney, J. (2010). Statistical and clinical significance, and how to use confidence intervals to help interpret both. Astralian Critical Care, 93(7), 93-97.

Tudor, K., Berger, J., Polivka, B. J., Chlebowy, R., & Thomas, B. (2014). Nurses’ perception of  family presence during resuscitation. American Journal of Critical Care, 23(6), 88–96.

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