Significance testing used in most social science experiments to transit from data to conclusions. This is inimical to scientific insight for several reasons. First the logic of significance testing revolves around null hypotheses that are almost always known apriori to be false. Second significance testing deemphasizes experimental power. Third, significance testing results a series of binary decisions (significant/not significant) that often ignore important patterns in the data. I discuss possible alternatives to significance testing..