As explained in another section, a probability value is not a measure of the size of an effect; it is a measure of how unlikely the data or more extreme data are under the assumption that the null hypothesis is true. If the dependent variable is measured in units that are meaningful in their own right, then the measurement of the size of the effect can be as simple as the difference between means. For example, if an experimenter were interested in how long it takes people to figure out how to use various designs for an automated teller machine (ATM), then measurement of the size of the effect would not pose much of a problem. If, on average, people can figure out how to use one design for an ATM in 100 seconds and an alternate design in 145 seconds, then it is clear that the effect size is 45 seconds. Or, if one wished, one could say that the latter design takes 45% longer than the former for users to figure out. Unfortunately, most of the dependent variables used in behavioral research are more difficult to interpret.