The central objective of the statistical analysis in the health programs system is to provide a good understanding and interpretation of the health issues. The study of no-parametric methods as applied in the study of healthcare system involves a better analysis behaviors of different groups using statistical approaches where the outcome of the result generated is ranked in the ordinal or nominal order ( Corrado, 1989). The ask of doing statistical analysis will involve the process such as applications of the parametric test, determine and analyze the implementation of different non-parametric hypothesis testing procedures and examine the type of results generated and variables as the overall sample.
Comparison of the parametric and non-parametric test will involve an integrated system where the data are divided into subgroups to define the significance of each variable in both statistical approaches. Typically, the parametric criteria are applied in many environments especially when finding the appropriate data source for generating accurate numerical references in the research study system. Hypothesis testing is the best approach used to test the significances of the non-parametric information especially when examining the bests data sequence in a specific population study. The sample collected is analyzed and tested using various techniques of a research study (Corrado, 1989). Statistical analysis of the data forms the Health Care Setting is creating an excellent scope to help have systematic methods of enhancing a superb sequence in analyzing the data. The general understanding of parametric and non-parametric data is ensuring efficient means of operating with significant data in the healthcare system.
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Nonparametric Test
The health care setting is forming an excellent platform for subsets of the health variables through parametric and non-parametric testing. The statistical trial involving the parametric data requires population parameter while non-parametric test helps the researcher study the information that has no idea of the population parameters. The assumptions made on the population parameter is known to have parametric test while nonparametric analysis the independent variable in the population systems. Technically, the parametric test involves a statistical test in the population distributions while nonparametric measures test the arbitrary case in the population system. Additionally, parametric approaches in verifying the data involve ratio and interval level while the nonparametric test consists of the measurement of the information using the nominal and ordinal scale of doing the analysis (Corrado, 1989). The central tendencies of the data involve a mean value in the data collected while in the other case the information tested is the nonparametric test on the median.
The nonparametric test on the data generated will involve testing the data in the population distribution while applying different system hypothesis testing. The critical application of the population parameters includes the cases where the population under study is generating a continuous case in the outcomes. Additionally, the measurement two independent variables where there is null hypothesis and alternative hypothesis. It is also to conduct nonparametric the population involved is having two test systems in the central tendencies interpretations of the population distribution (Corrado, 1989). Therefore, the outcome of the nonparametric test requires data that have parameters ranked in the ordinal measurements.
Hypothesis testing on the population from the nonparametric community will involve some approaches in analyzing two variables. The hypothesis on the two sample forms the target population when comparing the mean, medians, and standard deviations of the population's system. The explanation involves null hypothesis and alternative of the nonparametric data. The null hypothesis requires two communities in the healthcare system where the variables are equal in all distribution; the alternative explanation involves analysis of variables which are not identical ( Pesaran, & Timmermann, 1992). The hypothesis testing requires report indicates that the nonparametric population variables are not equal as oppose to the sample collected in the null hypothesis.
Nonparametric test in the data analysis helped in investigating the data to generate outcomes which would provide sufficient evidence that the health variables are not equal in the independent groups. I was thinking the results produced would create a better system of enhancing appropriate assumptions for the information of continuous outcome in the sample generated (Pesaran, & Timmermann, 1992). It is clear that the results generated in the hypothesis testing would create a positive trend in the sample collected from the target population of the healthcare settings. I believe my general understanding of the nonparametric testing is having the similar approaches when solving the relationship between two variables.
Conclusion
The statistical approaches in analyzing the healthcare setting information system have a reliable result. Therefore, the parametric and nonparametric test is sufficiently describing the medical variable with sufficient evidence of supporting the results generated in the hypothesis testing. Technically, the methods used in determining an excellent critique of the population analysis system. The depth of the hypothesis analysis involves a broad representation of all variables to help enhance the accuracy and reliability of the data generated. The investigation is creating a good position of comparing the means values of the involved variables. Typically, the scope of data includes a relative variability of the sample through the statistical methods, especially in the hypothesis testing modules (Pesaran, & Timmermann, 1992). The general organization of the paper is creating a better understanding of both parametric and nonparametric system that help in summarizing the assignments in the rank system. The impact of this article in the healthcare setting is increasing a better understanding of the difference between parametric and nonparametric data. Therefore, hypothesis testing is creating an excellent contextual facts development to change the general strategies implemented in the healthcare setting especially in the effort of achieving goals and objectives.
References
Corrado, C. J. (1989). A nonparametric test for abnormal security-price performance in event studies. Journal of financial economics, 23(2), 385-395.
Pesaran, M. H., & Timmermann, A. (1992). A simple nonparametric test of predictive performance. Journal of Business & Economic Statistics, 10(4), 461-465.
