In Technopoly, Neil Postman writes about a trick he loved to play on his fellow professors. When he greeted them in the morning, he would ask whether they had read the C-section of the paper today. If they responded that they had, he would continue making small talk and carry on as usual. If, however, they responded that they didn’t read the section, he would say, “There was a pretty incredible article about a study done at the University of Minnesota that showed that people who run three times a week are 4 times as likely to die of a heart attack as those who don’t.” He would then test their response, most were surprised but believing. Some asked about the university that performed the study. Few doubted.
This is what happens when there is a glut of information. None of us have a comprehensive model that allows us to sort out all the information that we receive. As a result, for many of us, if there is a scientific study, it must be true. Without a doubt, using a scientific model and statistics has helped us probe and learn more about the world than we would know otherwise. It certainly helps us dispel urban legends and old wife’s tales, but we are almost falling into belief on the other side of rationality — where anything with a scientific study makes something true. Rumor takes on a new credibility if we add something about a study to prove any claim — this is in fact how new urban legends continue to spread. From small sets of data come all manner of extrapolations.
We should not immediately believe whatever the latest study suggests. Neither should we ignore scientific studies. We should remain skeptical. Science has provided us with a myriad of benefits, but it also comes at the cost of producing more knowledge than we are capable of properly comprehending.
[Nor should we believe things only if they were studied. This excellent article describes the flip side of this coin. http://theamericanscholar.org/this-just-in/]