To the Editor:
After reading Dalton Conley’s piece on recent developments in social-science research (“The Data in Your Lap: How to Interpret Naturally Occurring Experiments,” The Review, December 19), I can only anticipate what other breakthroughs in human knowledge await us in upcoming issues. The world is round? Organisms invisible to the naked eye often cause disease? Conscious choices may be influenced by unconscious motives?
My point is that the important distinction between correlation and causation has probably been one of the fundamental premises of inferential statistics and social-science research for as long as anyone can remember, and one of the first things students learn in any decent introductory course in any relevant field. Of course, that distinction has often been observed in the breach by both producers and consumers of research -- not hard to do, when we consider that correlation is indeed a necessary condition of a causal relationship, even though it is not a sufficient one; that, as Conley notes, there are often legitimate barriers to true experiments in the social sciences; and that social scientists, like the rest of us, are often tempted to exaggerate the significance of their achievements. If scientists, social or otherwise, have stopped conflating correlation and causation so often, I’m very happy to hear it, but it’s hardly the “revolution” Conley cites.
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