In terms of main effects and interactions, which statement is accurate?

The statement that one can commonly observe main effects without the presence of interaction effects accurately reflects the nature of factorial designs in statistics. In experimental research, a main effect refers to the impact of one independent variable on the dependent variable, ignoring the other variables. Interactions occur when the effect of one independent variable on the dependent variable depends on the level of another independent variable.

It is entirely possible to have a significant main effect of one variable while the other variable does not alter this effect, indicating that interaction is not necessary for a main effect to exist. For instance, in a two-way ANOVA, you might find that one factor significantly influences the outcome irrespective of the levels of another factor. This emphasizes that main effects can be independent of interactions, allowing researchers to identify the individual impacts of various independent variables on the dependent variable without assuming their effects are interdependent.