**Reply to both discussions**
#1
Parametric and non-parametric tests are essential statistical tools used to analyze research data and answer research questions to reach an outcome. Both tests differ in how they interpret the research’s assumptions. According to Gray et al. (2016), the common assumption accompanying all parametric tests is that the data are normally distributed, such as if the data yield a bell-shaped curve or the groups in the study have equal variances. An example of a parametric test is the independent samples t-test. This test is appropriate when there is a need to compare the means of two independent groups, the data is approximately normally distributed, and the variances of the two groups are approximately equal (Hoskin, 2024). For instance, the samples t-test can be used to compare the average test scores of two different groups of students.
Unlike parametric tests, nonparametric tests do not rely on strict normal assumptions about the data, making them more effective in research scenarios with fewer assumptions. They are also adaptable and helpful in studies where the data does not follow a traditional bell curve (Gray et al., 2016). An example of a nonparametric test is the Mann-Whitney U test. This test is appropriate when the assumptions of the parametric tests are not indicated, such as when the data is not normally distributed or when the variances are not equal. It is used to compare the central tendencies of two independent groups. For example, we could use the Mann-Whitney U test to compare the median income of two populations (Gray et al., 2016).
References
Gray, J. R., Grove PhD RN ANP-BC GNP-BC, Susan K., & Sutherland, S. (2016). Burns and grove’s the practice of nursing research: Appraisal, synthesis, and generation of evidence (8th ed.). Saunders.
Hoskin, T. (2024, June 13). Parametric and Nonparametric: Demystifying the Terms [Mayo Clinic CTSA BERD Resource website]. https://www.mayo.edu/research/documents/parametric-and-nonparametric-demystifying-the-terms/doc-20408960.
#2
When populations employ ranking order, non-parametric test techniques are implemented. A non-parametric test would be suitable if your data is ranked, such as a movie rating system that ranges from one to five stars. The simplicity factor justifies the use of non-parametric techniques. Parametric methods may be suitable in certain circumstances; however, non-parametric methods may be more advantageous in others. Some statisticians believe that non-parametric approaches are less prone to misuse and misinterpretation because of their enhanced resilience and simplicity. In order to achieve the same level of confidence as a parametric test, a non-parametric test may necessitate a larger sample size. Among the several statistical tests frequently employed to ensure that the data sample is representative of the whole population is the parametric test. Parametric tests, such as the t-test and analysis of variance (ANOVA), are employed to compare means. These tests necessitate the fulfillment of specific assumptions, including normality and equal variance. It is reasonable to assume that the variances of two groups are equivalent when conducting evaluations. The null hypothesis for this assumption was that the variances of the different categories were equivalent. If the null hypothesis is not rejected, it suggests that the variances are identical. Conversely, the data were gathered from a population that was uniformly distributed, as predicated on the assumption of normality. Normal distributions exhibit numerous comparable characteristics. The distribution is symmetrical in relation to the mean if the mode, median, and mean are all equal (Orcan 2020). Utilizing both parametric and non-parametric tests, the effectiveness of metaheuristic algorithms in resolving the combined economic emission dispatch problem is assessed through experimental comparison (Jevtić et al.2021). They are capable of employing the framework they have developed for this project to resolve additional issues by utilizing the most optimal algorithm available.
References
Orcan, F. (2020). Parametric or Non-Parametric: Skewness to Test Normality for Mean Comparison. International Journal of Assessment Tools in Education.
Jevtić, M., Jovanović, N., & Radosavljević, J. (2018). Experimental Comparisons of Metaheuristic Algorithms in Solving Combined Economic Emission Dispatch Problem Using Parametric and Non-Parametric Tests. Applied Artificial Intelligence, 32(9–10), 845–857. https://doi.org/10.1080/08839514.2018.1508815
