Critical Analysis of Selected Article: "Nonparametric Statistical Methods in Medical Research"

Goals and Purpose of the Research Study

The chosen article by Schober and Vetter (2020) focuses on analyzing nonlinear trends in medical studies utilizing nonparetic statistic approaches. This research aims to establish and demonstrate the relevance of nonparametric techniques under situations where the conditions for applying parametric analysis are not met. Some of these assumptions are the normality and homoscedasticity of the data while conducting the analysis, both of which are unlikely to hold in medical research. The study's objectives are to establish when and why nonparametric techniques should be used and how they bring higher accuracy and reliability than parametric methods under given circumstances.

Application of Nonparametric Tests of the Research Study

Applied in medical research, the article uses nonparametric tests like the Mann-Whitney U test, the Kruskal-Wallis test, and Spearman's rank correlation test. These tests are used for ordinal or nonparametric data; they do not follow a normal distribution(Kim et al., 2018). For example, the Mann-Whitney U test is used to compare two independent groups where the variables are measured on ordinal data. From these nonparametric tests mentioned in the article to conducts of nonparametric tests, they are valid even if the data does not satisfy the constraints of a parametric test.

Inappropriateness of Parametric Methods

Other automated methods for example t-tests, analysis of variances assumes the data to be warranting certain conditions such as normality and homogeneity of variance. According to Schober and Vetter (2020), these assumptions are frequently unmet in medical studies because the data characteristics are skewed, ordinal, or have unequal variances. For instance, it would be wrong to apply a t-test to compare two samples where data contains a non-normal distribution because of the risk of making incorrect conclusions. These are the situations when material is generated from more complex populations, and nonparametric methods are more appropriate to maintain the validity of the statistical analysis since they do not impose such strict assumptions.

Strengths and Weaknesses of the Research Study

The research study does an excellent job of spelling out when nonparametric methods should be applied, and such information is quite valuable to those academics in medical research. The study explains the use of the various nonparametric tests and the functionality of each test (Gray & Grove 2020). However, a possible limitation is that the examples or demonstrations that have explained how to use those methods in the context of medical research are not included. Even integrating more specifics of case studies could have strengthened the study because it would have informed the reader with experience of how nonparametric techniques are applicable.

Application to Evidence-Based Practice in Nursing

Schober and Vetter's study (2020) can provide helpful findings and recommendations for advanced nursing practice based on empirical research. Through this, the study involves recommending nonparametric methods when they are appropriate, and this urges nurses and other healthcare professionals to select statistical techniques that will give more valid and reliable results, especially in cases where data will not satisfy the assumptions of parametric tests (Baghi & Kornides 2013). This procedure can enrich the quality of the research done in nursing. As a result, there will be positive changes in clinical decision-making practices and positive impacts on patients' results. Also, focusing on the study's uses and correct interpretations of statistical methods can help enhance DNP-prepared nurses' confidence in research processes and combining empirical findings into practice.

Questions:

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