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Science relies heavily on quantification. Increasingly precise measurement is a hallmark of scientific progress. Scientists seek to measure important things, and the association between precise measurement and scientific importance furnishes a heuristic assumption that what is measured with great precision must be highly important. The heuristic association between importance and precision can be misleading, however. Quantity of life can be measured much better than quality, and so public policy has focused much more on improving quantity than quality of life. When I ask people whether they would relinquish all pleasures in order to live 300 years, they laugh and say no, but each time some pleasure is linked to shorter lifespan, people start to eliminate it from their lives. Half a century ago, psychologists emphasized significance testing as a binary judgment as to whether an experimental result was significant or not. The purpose of an experiment was to establish whether a causal relationship existed between the independent and dependent variables. In that context, the purpose of significance testing was to avoid being wrong too often. Recently, however, psychological scientists have shifted toward Abstract The artificial environment of a psychological laboratory experiment offers an excellent method for testing whether a causal relationship exists, — but it is mostly useless for predicting the size and power of such effects in normal life. In comparison with effects out in the world, laboratory effects are often artificially large, because the laboratory situation is set up precisely to capture this effect, with extraneous factors screened out. Equally problematic, laboratory effects are often artificially small, given practical and ethical constraints that make laboratory situations watered-down echoes of what happens in life. Furthermore, in many cases the very notion of a true effect size (as if it were constant across different manipulations and dependent variables) is absurd. These problems are illustrated with examples from the author’s own research programs. It is also revealing that experimental effect sizes, though often quite precisely calculated and proudly integrated into meta-analyses, have attracted almost zero attention in terms of substantive theory about human mental processes and behavior. At best, effect sizes from laboratory experiments provide information that could help other researchers to design their experiments, — but that means effect sizes are shop talk, not information about reality. It is recommended that researchers shift toward a more realistic appreciation of how little can be learned about human mind and behavior from effect sizes in laboratory studies.

Do Effect Sizes in Psychology Laboratory Experiments Mean Anything in Reality?