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In the design of experiments and analysis of variance, a main effect is the effect of an independent variable on a dependent variable averaging across the levels of any other independent variables. The term is frequently used in the context of factorial designs and regression models to distinguish main effects from interaction effects. The main effects of a factor are contrasts between levels of one factor averaged over all levels of another factor whilst the interaction effect measures differences between the simple effects of one factor at different levels of the other factor (this may also be called a difference of differences). However the main effect is sometimes not much useful whenever there is an interaction effect since interaction examines whether the levels one factor influence the performance across the levels of another factor. Relative to a factorial design, under an analysis of variance, a main effect test will test the hypotheses expected such as H0, the null hypothesis. Running a hypothesis for a main effect will test whether there is evidence of an effect of different treatments. However a main effect test is nonspecific and will not allow for a localization of specific mean pairwise comparisons (simple effects). A main effect test will merely look at whether overall there is something about a particular factor that is making a difference. In other words a test examining differences amongst the levels of a single factor (averaging over the other factor and/or factors). Main effects are essentially the overall effect of a factor.