Chat with us, powered by LiveChat GCU The Statistical Data Obtained from Underlying Population Discussion - STUDENT SOLUTION USA

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Non-parametric and parametric tests differ first in the variable outcome levels they can measure; specifically, nominal- and ordinal-level outcome variables can only be measured with a non-parametric test, while interval- and ratio-level outcome variables can be measured with either parametric or non-parametric tests (Corty, 2016, p. 768). However, parametric tests are more restrictive in that they can only measure interval- and ratio-level variables for which assumptions about the shape of the population have been met (Corty, 2016, p. 768). Parametric tests are preferred because they provide more statistical power and increase the likelihood of rejecting the null hypothesis; however, if non-robust assumptions are not met, or if the data is at the nominal or ordinal level, the non-parametric test can be used (Corty, 2016, p. 768). Although they provide less statistical power and make researchers less likely to reject the null hypothesis, non-parametric tests is beneficial in that it is less restrictive than the parametric test in terms of data level and population assumptions.

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