Parametric and Nonparametric Statistical Tests Essay
Parametric and Nonparametric Statistical Tests Essay
Nursing practice problems vary in complexities, manifestations, and impacts. Nurses have a professional mandate to study these problems and offer practical, evidence-based solutions. The process starts with formulating a hypothesis followed by a problem-centered intervention. To prove or reject the hypothesis, researchers must collect adequate data. The data is then subjected to statistical tests to determine whether there is sufficient evidence to prove or disprove the hypothesis (Cooksey, 2020). The tests could be parametric or nonparametric, depending on the data.
Parametric statistical tests are based on assumptions about the population’s distribution from which the sample was taken. The sample needs to be adequate to represent the population effectively. In parametric statistics, the information about the population’s distribution is known (Verma et al., 2019). Such information is also based on a fixed set of parameters. Interval and ratio data are subjected to parametric statistics since their distributions can be predicted. On the other hand, nonparametric statistics are not based on assumptions. As such, data is collected from a sample and do not follow a specific distribution (Kvam et al., 2022). The parameters are not fixed, implying that the information about the population’s distribution is unknown. This makes it necessary for the researcher to test the population’s hypothesis.
Statistical tests depend on the data collected and underlying assumptions. Regarding normality, parametric data is assumed to be normally distributed, hence a bell-shaped curve (Mishra et al., 2019). As a result, the most appropriate thing for the researcher, if the data are not normally distributed, is to perform a nonparametric statistical test. Nominal and ordinal data are nonparametric and require nonparametric statistical tests since they do not assume any particular distribution.
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References
Cooksey, R. W. (2020). Illustrating statistical procedures: Finding meaning in quantitative data. Springer Singapore.
Kvam, P., Vidakovic, B., & Kim, S. J. (2022). Nonparametric statistics with applications to science and engineering with R (Vol. 1). John Wiley & Sons.
Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of Cardiac Anaesthesia, 22(1), 67–72. https://doi.org/10.4103/aca.ACA_157_18
Verma, J. P., & Abdel-Salam, A. S. G. (2019). Testing statistical assumptions in research. John Wiley & Sons.
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Topic 3 DQ 2
Compare parametric and nonparametric statistical tests. How does each test depend on the assumption of normality? What would you do if the data are not normally distributed? Provide evidence supporting your response.
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