NR 716 Week 6 Using Non-Parametric Statistical Tests

Student Name

Chamberlain University

NR-716: Analytic Methods

Prof. Name

Date

Using Non-Parametric Statistical Tests

Discussion

Purpose

The primary purpose of this discussion is to deepen the understanding of non-parametric statistical tests and their importance in healthcare research. Non-parametric tests are especially valuable when data do not meet the assumptions required for parametric tests, such as normality and homogeneity of variance. In clinical research, small sample sizes and skewed data distributions are common, which makes non-parametric approaches vital for obtaining valid and reliable results. By critically analyzing the use of these tests, researchers can determine whether findings are methodologically sound and suitable to guide evidence-based practice and clinical decision-making.

Instructions

As practice scholars, it is essential to evaluate research evidence thoroughly before applying it in clinical settings. In the given scenario, a quasi-experimental study examines correlations between variables. However, the dataset is small and non-normally distributed, yet the researchers used Pearson’s r correlation — a method that assumes normality. This choice raises concerns about the appropriateness of the statistical method and the reliability of its findings.

To address these concerns, the following questions are explored:

Question 1: In your appraisal of the evidence, you note that a Pearson’s r correlation is used to analyze data. Is this the correct level of correlational analysis? Explain your rationale.

Pearson’s r correlation is a parametric test that requires assumptions such as linearity, normal distribution, and homogeneity of variance. Given that the study’s dataset is small and not normally distributed, Pearson’s r is not an appropriate statistical test in this case. Using it under these conditions can lead to misleading or invalid conclusions.

A non-parametric alternative, such as Spearman’s rank-order correlation (rho) or Kendall’s tau, should be used instead. These methods measure the monotonic relationship between variables without requiring data normality. For example, Spearman’s rho assesses whether higher values in one variable correspond to higher (or lower) values in another, regardless of linearity.

Applying non-parametric tests enhances the credibility, accuracy, and validity of findings, supporting more dependable evidence-based practice decisions.

Question 2: Are association and correlational analysis equivalent in determining relationships between variables?

Although the terms association and correlation are sometimes used interchangeably, they are not equivalent. The difference lies in their scope and statistical application, as summarized below:

In essence, association can be observed without formal testing (e.g., noticing that two variables appear related), while correlation quantifies this relationship using statistical procedures. Therefore, correlation provides a precise and measurable evaluation of association.

Question 3: Do these findings impact your decision about whether to use this evidence to inform practice change? Why or why not?

Yes, these findings have a direct impact on the decision to use this evidence for practice change. Evidence-based practice depends on research that employs sound methodology. If an inappropriate statistical test, such as Pearson’s r, is applied to data that are non-normal and small in size, the validity of results becomes questionable. This uncertainty undermines confidence in using the findings to guide clinical improvements.

Before integrating such evidence into practice, the study should be reanalyzed using non-parametric methods such as Spearman’s rho or Kendall’s tau. Conducting appropriate statistical testing ensures that results are valid and trustworthy for informing evidence-based clinical interventions.

Program Competencies

This discussion supports the following program competencies:

• Integration of scientific knowledge into daily clinical practice (POs 3, 5).

• Application of analytical methods to convert research evidence into clinical innovations (POs 3, 5).

• Evaluation of information systems and technologies to enhance healthcare delivery (POs 6, 7).

• Policy analysis to promote social justice and equitable healthcare access (POs 2, 9).

• Translation of research findings into preventive strategies for improving population health (PO 1).

• Leadership in professional identity and judgment, fostering resilience and accountability in clinical environments (POs 1, 4).

Course Outcomes

Upon completing this discussion, learners are expected to:

• Critically evaluate statistical methods to improve evidence appraisal (PCs 1, 3, 5; POs 3, 5, 9).

• Analyze both research-based and non-research data to support decision-making and practice translation (PCs 1, 3, 4, 5, 7, 8; POs 1, 3, 5, 7, 9).

References

Conover, W. J. (1999). Practical nonparametric statistics (3rd ed.). Wiley.

Polit, D. F., & Beck, C. T. (2021). Nursing research: Generating and assessing evidence for nursing practice (11th ed.). Wolters Kluwer.

NR 716 Week 6 Using Non-Parametric Statistical Tests.

Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763–1768. https://doi.org/10.1213/ANE.0000000000002864