believe some small violations of the homogeneity of variance assumption may have little practical effect on the analysis due to the robust nature of parametric tests. However, other researchers believe any violation of the homogeneity of variance assumption can result in tarnished or unbelievable results regardless of the robust nature of parametric tests. In this discussion thread, discuss the viability or falsity of both viewpoints and how you would approach one view point versus the other. Homogeneity of variance assumes that the F test and the t tests in a population shows variances in the distribution or spread of the data around the mean of at least two samples which are considered the same. Variance speaks to a measure of how the data is spread out (DeMoulin, 2014). There can be an assumption surrounding homogeneity of variance when the p-value has a figure that is greater than .05. When this is the case, the research practitioner would have noted the assumption of homogeneity of variance and is able to carry out a one-way ANOVA. However, if the p-value is smaller than .05 the research practitioner would have been in violation of the assumption of homogeneity of variance and therefore would be required to use non=parametric test ( e.g. Welch's). This is done to determine if there is any statistical significance observed. F test I used to determine if sample is homogenous or heterogeneous. It tells if the variance of the two populations is equal. This may require the use of a one-tailed or two-tailed test. Parametric tests have a "bell shaped curve" or a normal distribution curve (Parametric test, 2019). These tests are deemed to be more powerful when compared to non-parametric test and will see researcher utilising a sample size that is small (Parametric test, 2019). In dealing with parametric tests, they are seen as more robust as the assumption of violations are miniscule and has little or no violation on the results in a research. The assumption is that of homogeneity of variance assumes that all comparisons in the t test and ANOVA possess the same variance, "The independent samples t-test and ANOVA utilize the t and F statistics respectively, which are generally robust to violations of the assumption as long as group sizes are equal" (The Assumption, 2012). If there is not a significant difference in group size or a small sample size therefore there is a lack of homogeneity (The assumption, 2021)