Does psychology require more (refined) data?
Experimental psychology is a discipline that collects data to advance knowledge, foster theories and falsify wrong leads. It is a very fertile scientific field, collecting possibly billions of new data every year. Researchers often struggle hard to come up with innovative designs but this richness in methodological sophistication is not mirrored in the behavioral measures collected. Too often, research articles report preferences (or percent correct if there is a best choice) and mean response times. How many experiments, as clever as they might be, will it take to unravel the mysteries of the mind when there are only two variables observed? Here, we will argue that a lot of information is wasted in psychology. Even if it all starts with preferences and response times, averaging is only the beginning. We will discuss the importance of variances and skew in response times. We will also argue in favor of coefficients of changes. These are just examples; we wish to encourage researcher to use the same amount of imagination on measurement as on experimental design to stop the big datawaste.