The original post provides valuable insight into basic descriptive statistics and how they can be calculated. The discussion relies on the hypothetical case study and correctly determines the percentage, mean, and median values, as well as the standard deviation. According to Conner and Johnson (2017), these are the typical values that are used in mathematics and other research fields. There is no doubt that such descriptive statistics can be used to determine the effectiveness of the intervention. Thus, it is perfect that the original post explicitly comments on this fact by comparing the pre-intervention and post-intervention mean values. The identified difference of.46 points reveals that the proposed intervention was more effective, and it is excellent that this fact was covered in the discussion. In other words, the original post identified the descriptive statistics and highlighted the effectiveness of the intervention.
In addition to that, I would like to enrich the discussion regarding outliers. According to Conner and Johnson (2017), these are the points that are distant from most observations, and they can emerge because of measurement error or variability in a sample size. That is why it is not a surprise that a single outlier can affect the mean values and the original post comments on how patient #10 influences the pre-intervention and post-intervention average scores. As a result, Ph.D. researchers and DNP-prepared nurses should know what to do with these phenomena. Possible options include using complex formulas to balance outliers or excluding them from further analysis (Conner & Johnson, 2017). Consequently, it is reasonable to draw sufficient attention to all the outliers to ensure that they do not lead to incorrect descriptive statistical values.
Reference
Conner, B., & Johnson, E. (2017). Use these tools to analyze data vital to practice-improvement projects. American Nurse Today, 12(11), 52-55.