Why Homoscedasticity Is Important?

Homoscedasticity, or homogeneity of variances, is an assumption of equal or similar variances in different groups being compared. This is an important assumption of parametric statistical tests because they are sensitive to any dissimilarities. Uneven variances in samples result in biased and skewed test results.

What happens when homoscedasticity is violated?

Heteroscedasticity (the violation of homoscedasticity) is present when the size of the error term differs across values of an independent variable. ... The impact of violating the assumption of homoscedasticity is a matter of degree, increasing as heteroscedasticity increases.

What does homoscedasticity mean?

Homoskedastic (also spelled "homoscedastic") refers to a condition in which the variance of the residual, or error term, in a regression model is constant. That is, the error term does not vary much as the value of the predictor variable changes.