What is the difference between main effects and interaction effects in ANOVA?

The distinction between main effects and interaction effects in ANOVA is critical in understanding how independent variables (factors) contribute to the dependent variable. Main effects refer to the individual impact of each independent variable on the dependent variable, analyzed in isolation, without considering the influence of other variables. For instance, if you have two independent variables, the main effect of each variable provides insights into how changes in that specific variable affect the outcome.

In contrast, interaction effects occur when the effect of one independent variable on the dependent variable is influenced by the level of another independent variable. This means that the relationship between one factor and the outcome can change depending on the level of the other factor. Understanding these interactions is crucial for a comprehensive analysis, as they reveal deeper relationships that may not be apparent when looking at the main effects alone.

Thus, the correct choice articulates that main effects analyze individual variables only, reflecting their independent contribution to the outcome measure. It highlights the foundational principle of separating the individual influences of variables before considering more complex interactions that may arise in a multifactorial context.