This course offered opportunities to learn about descriptive, inferential, and qualitative data analysis, including their similarities and differences. Descriptive analysis serves to present information about a fully measured population in a structured, categorized, and accessible form (Nieswiadomy & Bailey, 2018). Inferential analysis uses the data from a randomized sample to draw conclusions about the entire population, usually to estimate population parameters and test hypotheses (Nieswiadomy & Bailey, 2018). Qualitative analysis, as follows from the name, works with more subjective and less quantifiable data to identify themes in highly personalized sources. An important and interesting difference between the first two and the latter is that, in descriptive and inferential analysis, relevant parameters are identified before starting data fathering (Nieswiadomy & Bailey, 2018). In contrast, in qualitative analysis, relevant categories only emerge after the data gathering and during the analysis itself. In any case, data analysis is crucial for discovering credible findings in nursing because data can only be conducive to achieving health goals if the researchers interpret it correctly.
The difference between statistical and clinical significance is paramount to understand in nursing research. Statistical significance means that the null hypothesis has been rejected, and the numerical differences on one or more variables between groups within the chosen sample are real (Nieswiadomy & Bailey, 2018). In other words, statistical significance means that the differences in the parameters measured are not due to sheer chance. Clinical significance means that the data is not merely real but relevant for application in a clinical setting (Nieswiadomy & Bailey, 2018). It is more important when considering the application of findings to nursing practice because the statistically correct data that is not relevant to achieving goals set in a clinical setting cannot be applied.
Nieswiadomy, R. M., & Bailey, C. (2018). Foundations of nursing research (7th ed.). Pearson.