Calling Bullshit: The Art of Skepticism in a Data-Driven World

by Carl T. Bergstrom

But it is rarely straightforward to figure out what effects an action will have. A large fraction of the time all we have to work with is information about correlations. Scientists have a number of techniques for measuring correlations and drawing inferences about causality from these correlations. But doing so is a tricky and sometimes contentious business, and these techniques are not always used as carefully as they ought to be. Moreover, when we read about recent studies in medicine or policy or any other area, these subtleties are often lost. It is a truism that correlation does not imply causation. Do not leap carelessly from data showing the former to assumptions about the latter.*4 This is difficult to avoid, because people use data to tell stories. The stories that draw us in show a clear connection between cause and effect. Unfortunately, one of the most frequent misuses of data, particularly in the popular press, is to suggest a cause-and-effect relationship based on correlation alone. This is classic bullshit, in the vein of our earlier definition, because often the reporters and editors responsible for such stories don’t care what you end up believing. When they tell you that drinking wine prevents heart disease, they are not trying to lead you into alcoholism or away from behaviors that promote cardiac health. At best, they are trying to tell a good story. At worst, they are trying to compel you to buy a magazine or click on a link.