Journal Quiz 3 Question: As you continue preparing for your upcoming proposal defense, based on the design of your project, which tests have you selected to perform to determine significance? Discuss the use of testing in DNP projects.

Module 3 Journal Quiz Question: Discuss the use of testing in DNP projects

The use of statistical significance testing in DNP projects is crucial in validating the results and determining whether they reflect reality. There are various tests can be conducted to determine the statistical significance of a research study. One of the tests is the Wilcoxon signed-rank test. The Wilcoxon test can be described as a non-parametric test that is comparable to the dependent t-test (Jiang et al., 2020). The significance test compares two data sets resulting from the same group of participants. Such a situation occurs when there is a need to compare score changes from one-time point to another. The tests would be appropriate for the pre-post design study that seek to compare the difference in outcomes before and after the intervention is implemented. Wilcoxon paired signed rank is usually a common alternative utilized to the paired two-sample t-test.

                The null hypothesis in a Wilcoxon signed-rank test is that there is no difference in the two paired reported scores. Major steps taken to determine the significance level include computing difference scores for the two paired values, assigning ranks from the smallest absolute differences to the largest, providing signs based on the direction of the differences, setting T, which is the absolute value of positive or the negative rank sum, and conducting the signed-rank test to obtain the Wilcoxon test statistic (W). To determine whether the results obtained are statistically significant, there should be a comparison between the observed value and the critical value of W. For the null hypothesis to be rejected, the observed W value must be equal to or less than the critical W value (Jiang et al., 2020).


Jiang, Y., Lee, M. L. T., He, X., Rosner, B., & Yan, J. (2020). Wilcoxon Rank-Based Tests for Clustered Data with R Package clusrank. Journal of Statistical Software96, 1–26.

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