Descriiptive analysis (statistics) helps to describe a given data set. It helps

Descriiptive analysis (statistics) helps to describe a given data set. It helps to summarize the data and uses several methods to do so, including but not limited to: percentages, proportions, mean, graphs and charts. Inferential analysis (statistics) analyzes sample data to predict and extrapolate what it would mean for a larger population. Inferential analysis can be used in healthcare to test new medications and drugs. Qualitative analysis (results) analyzes the data by interpreting the meaning. An interactive approach is used such as interviews and one-on-one conversations to gain an understanding of patient’s thoughts, feelings, and experiences. “In a health behaviour study whereby health or education policies can be effectively developed if reasons for behaviours are clearly understood when observed or investigated using qualitative methods” (Wong, 2008).
Something that I learned that is interesting to me is that healthcare uses inferential analysis in many ways. In the example of introducing new medications, healthcare must extrapolate data from the analysis to let the public know what the potential side effects and adverse effects might be for an individual taking a newly introduced medication. Since humans are all different, it is impossible to know how all individuals might react to taking a medication that has been newly introduced in the market. Similar to the covid vaccination that was fast-tracked to get it to the public, all the side and adverse effects are unknown since the clinical trials were minimal.
I believe data analysis is necessary for discovering credible findings for nursing because nursing needs to be based on fact and evidence based practice. Nursing is grounded in facts and although it is impossible to know all potential outcomes, it is crucial that nursing uses analysis to predict potential outcomes in order to do the best possible job.
Clinical significance studies its “impact on clinical practice” and what the outcome will be while statistical significance suggests the “reliability of the study results” (Ranganathan, et al., 2015). What is interesting to me is that just because the outcome has been proven to be statistically significant, it doesn’t necessarily mean that it is clinically significant. As it relates to application of findings for nursing practice, I believe clinical significance is more important. For something to be clinically significant, you need to have knowledge and experience in the field of nursing. For a non-nursing professional with no experience in healthcare it would be impossible to know if an outcome is clinically significant since the person would have zero depth of understanding. ( 1-2 short Paragraph)