What does a non-parallel line graph indicate in relation to interaction effects?

A non-parallel line graph is indicative of an interaction effect because it shows that the relationship between one independent variable and the dependent variable changes depending on the level of another independent variable. In simpler terms, when the lines on the graph do not run parallel to each other, it suggests that the effect of one factor on the outcome varies based on the level of another factor. This is the hallmark of an interaction.

In the context of this question, the presence of non-parallel lines illustrates that the influence of one variable isn't consistent across all conditions of the other variable, which confirms that an interaction effect exists. Understanding interaction effects is vital because they reveal more complex relationships in the data beyond what can be captured by examining main effects alone.

Other potential responses do not accurately describe the implications of a non-parallel line graph. For example, stating there are no significant main effects would not be appropriate since interaction effects can occur alongside main effects. Recognizing multiple interaction effects might misinterpret the graph's indication, as the non-parallel lines themselves primarily denote one interaction rather than multiple. Finally, claiming that it represents only one grouping variable misses the necessity of examining interactions between multiple variables.