Homogeneity of variance is an assumption underlying both t tests and F tests (analyses of variance, ANOVAs) in which the population variances (i.e., the distribution, or “spread,” of scores around the mean) of two or more samples are considered equal. In correlations and regressions, the term “homogeneity of variance in arrays,” also called “homoskedasticity,” refers to the assumption that, within the population, the variance of Y for each value of X is constant. This entry focuses on homogeneity of variance as it relates to t tests and ANOVAs.
Within research, it is assumed that populations under observation (e.g., the population of female college students, the population of stay-at-home fathers, or the population of older adults living with type 2 diabetes) will be relatively similar ...
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