Qualitative Analysis In Nursing

The evidenced-base practice approach in nursing is a standardized method of obtaining and implementing clinical findings, and as professionals, we look thoroughly into research using few essential components necessary to comprehend it adequately. Therefore, it is imperative to familiarize yourself with statistical terminologies like confidence interval and statistical and clinical significance to benefit from the information acquired from a conducted study. First, to discuss the confidence interval (CI), one must apprehend the implications of this measure which could be explained as a process of initially taking a random sample from a population and computing a statistic, such as the mean from the data, while assessing the average of the population. Consequently, that measure prompts us to ask a vital question: How well the sample statistic estimates the underlying population value, or it is only relevant to the original specimen? Subsequently, the confidence interval is a measurement which provides a range of values likely to contain the population parameter of interest.("What are confidence intervals?,"n.d.)
Another way to put it is that " confidence interval includes information about the magnitude of effect and uncertainty surrounding it" (Hirpara, Jain, Gupta & Dubey, 2015).
Conversely, 95% CI is the most commonly used, and as Fethney (2010) explains it would indicate that " there is a 95% probability that the interval bounded by lower and upper limit contains 'true' population value" (p.95). With that in mind, the CI reveals the stability of the estimate evidenced by the proximity to the same value if the study was repeated multiple times (Confidence Intervals, n.d.). Furthermore, the purpose of CI is to confirm a clinical significance of a study which translates to the effectiveness of the implemented
intervention. Secondly, it is crucial to understand the meaning of the statistical significance, which Connely (2014) defines as " the probability that an effect seen in a study is not likely to be due to chance variation" (p.118). To clarify this statement would be helpful to realize that the relationship between variables considered in the study could be due to normal fluctuations or having a possible relationship. Furthermore, significance level does not demonstrate the degree of the correlation between a control group and experimental group suggesting that the study statistically significant does not necessarily conclude that it is clinically relevant. For example, diabetes patients take medication which lowers A1C by 0.2 percent which might be statistically significant; however, from the practical point of view, this is not an important finding. Besides, reducing A1c by 0.2 percent would not be clinically valuable to achieve the desired goal of preventing from microvascular or macrovascular complications because as Hirpara et al. (2015) explain: "It is the size of the effect that determines clinical importance and not the presence of statistical significance." Under those circumstances, one of the discrepancies surrounding statistical and the clinical significance came to light evident by a realization that researchers and clinicians should come to an agreement suggesting a study having a clinical and statistical significance correspondingly.
In conclusion, it is imperative for medical practitioners to be aware what the meaningful completion of a research entails, does the intervention in a study affect patient care or is it only a numerical value proving a minor change with scientific documentation. Therefore, a careful differentiation between these two mathematical principles would support professionals in making an appropriate clinical decision as well as provide confirmation that the intervention is worth commitment for the patient's benefit. After all, what is the most important in our profession is to make sure that patients take priority in our practice and not statistical data.