Please read below and write rebutal

Please read below and write rebutal

Hypothesis testing involves creating a null hypothesis and alternative hypothesis, comparing the data supporting each, and drawing inferences from the population parameter, i.e. mean, proportion, standard deviation, or variance (Evans, 2013, p. 139).  The inference is based on substantial statistical data to either accept the null hypothesis or the alternative hypothesis, ultimately rejecting one (Evans, 2013, p. 139).  A hypothesis test begins with creating a hypothesis, selecting a level of significance, selecting a decision rule for conclusion basis, collecting data and running statistical test, ending with selecting a conclusion based on the application of the decision rule to the statistic test (Evans, 2013, p. 140).  The hypothesis test is similar in nature to the scientific theory because its processes can be replicated by numerous researchers (Bradlow, 2008).
Analysis of variance (ANOVA) tests the differences of more than one mean based on factors such as education (Evans, 2013, p. 158).  ANOVA results in a measure of variance in the means of the groups and the variance within the groups and indicates the validity of the null hypothesis if the variance between groups is small (Evans, 2013, p. 159).  Three assumptions are necessary for parametric ANOVA calculations for the groups or factors:  they are randomly and independently gathered, normally distributed, represent equal variances (Evans, 2013, p. 159).  A nonparametric test does not rely on these assumptions to analyze more than one population mean against other population means (Evnas, 2013, p. 160).  Hill, Padmanabhan, & Puri (1988) shows that while nonparametric tests are occasionally inferior to parametric tests, nonparametric tests can also be superior for light-tailed and skewed distribution samples.
References
Bradlow, E. T. (2008, May). The Scientific Process at Its Best. Marketing Science. p. 323.
Evans, J. R. (2013).  Statistics, Data Analysis, and Decision Modeling.  Upper Saddle River:  NJ:  Pearson Education.
Hill, N., Padmanabhan, A., & Puri, M. L. (1988). Adaptive Nonparametric Procedures and Applications. Journal of The Royal Statistical Society: Series C (Applied Statistics), 37(2), 205.