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Data is a crucial element in all decision making processes in the healthcare industry. Similarly, nursing is a science field, and therefore, the success of all related activities relies on the process of information collection and its application. However, the gathered data can only be classified as authentic and reliable if a proper analysis approach is used. Consequently, inferential analysis, descriptive analysis, and qualitative analysis are examples of approaches, which can be applied to effectively conduct studies in nursing. Understanding the different methods gives a researcher the necessary skills to obtain substantial results.
Throughout my interactions with the class materials, I learned a few things about the various approaches to data analysis. Descriptive statistics are important in improving our understanding and description of various aspects of the collected information by giving precise observations and summaries in form of patterns. Inferential analysis, on the other hand, seeks to draw more findings from the collected sample besides observation and is essential in formulating predictions and theories on a population (Schober et al., 2018). Qualitative studies are different from the other two because they are more detailed and are concerned with why and how a certain phenomenon occurs and not just the description. All these data analysis methods are necessary for conducting successful nursing studies.
Furthermore, a data analysis process leads to findings or results which are applied in making accurate research conclusions. However, before using them it is important to understand their statistical and clinical significance. The former is used to illustrate that an event never happened by chance while the latter is used to justify the extent to which it occurred by proving a positive (Schober et al., 2018). Whereas both aspects are instrumental in nursing decision-making processes, clinical significance is more meaningful to me because statistical evidence might not always be accurate considering sample sizes can be too large or too small. Data analysis is an integral part of nursing studies and it is imperative to learn all related concepts.
References
Schober, P., Bossers, S., & Schwarte, L. (2018). Statistical significance versus clinical importance of observed effect sizes. Anesthesia & Analgesia, 126(3), 1068-1072. Web.
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