This is achieved by partitioning the total variance in a measured outcome into its sources: The components that are due to differences between means (SS Effects); which means variance that can be explained, such as by a regression model or an experimental treatment assignment and a component that is due to true random error (SS Error), which means a variance that cannot be explained.