The accuracy of parametric statistical tests is largely based on the data distribution of the collected data. Parametric tests are based on distribution assumptions, such as normality, linearity, equality of variances, etc. These assumptions and others vary based on the statistical test; therefore, it is critical for quantitative researchers to evaluate the assumptions pertaining to their statistical analyses and identify actions taken if assumptions are grossly violated.
Review the Lumley et al. (2002) article, as well as Lessons 19–21 and 24 in the Green and Salkind (2017) text. Use the Walden Library databases to identify a research example using your doctoral research proposal and consider the role and importance of the assumptions underlying each parametric test.
Post a comparison of one-sample, paired-samples, and independent-samples t-tests within the context of quantitative doctoral business research. In your comparison, do the following:
Describe the research example related to your doctoral research proposal.
Describe a hypothetical example appropriate for each t-test, ensuring that the variables are appropriately identified.
Analyze the assumptions associated with the independent-samples t-tests and the implications when assumptions are violated.
Explain options researchers have when assumptions are violated.
