![]() ![]() ![]() Given the intentionally intuitive nature of our silly example, the consequence of disregarding the interaction effect is evident at a passing glance. ![]() That’s not a good choice despite what the main effects show! When you have statistically significant interactions, you cannot interpret the main effect without considering the interaction effects. We’ll make our decision based on the main effects plots below.īased on these plots, we’d choose hot dogs with chocolate sauce because they each produce higher enjoyment. However, imagine that we forgot to include the interaction effect and assessed only the main effects. Suppose we want to maximize satisfaction by choosing the best food and the best condiment. In the previous example, you can’t answer the question about which condiment is better without knowing the type of food. When you have statistically significant interaction effects, you can’t interpret the main effects without considering the interactions. Overlooking Interaction Effects is Dangerous! Which condiment is best? It depends on the type of food, and we’ve used statistics to demonstrate this effect. If you put mustard on ice cream or chocolate sauce on hot dogs, you won’t be happy! Conversely, satisfaction levels are higher for mustard when the food is a hot dog. The graph shows that enjoyment levels are higher for chocolate sauce when the food is ice cream. The crossed lines on the graph suggest that there is an interaction effect, which the significant p-value for the Food*Condiment term confirms. ![]()
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