An interaction in a factorial design occurs when "the effect of one independent variable depends on the level of another independent variable" (McBurney, 2004, p. 290). The notation for an interaction in a 2 x 2 factorial design is A x B. In our example, an A x B interaction would occur if the effect of the drug (Factor A) depended on whether or not subjects were led to believe that the task was difficult (Factor B), or alternatively, if the effect of the task description (Factor B) depended on whether or not the subjects took the drug (Factor A). You may have noted that this is true for the data presented above in Table 5. In the graphic display of that data in Figure 3, the left panel shows that while on average (dashed red line) errors increased across the two levels of Factor A, this effect depended on whether or not the subjects were told that the task was "hard." When subjects were lead to believe that this was true (line B2), the drug greatly enhanced the number of errors; however, when they were told nothing about the task difficulty (line B1), the drug had no effect. Thus, it would be misleading to conclude that the drug impeded performance on the memory task; we must qualify our conclusion and state that the drug did so only when subjects were told that the task was "hard." "Whenever there is an interaction in the data, the main effects cannot be interpreted without discussing the interactions" (McBurney, 2004, p. 291).