What Causes What? Charles Wheelan Talks Causation In Stats On NPR
It is important to realize this distinction so that we can make better sense of the studies we read and the decisions we make. One example he provides in the interview is an experiment regarding the effects of estrogen replacement therapy on women. The initial study discovered what seemed to be a logical causal relationship between an increase of estrogen and the improved health of the women surveyed. However, it turned out that the positive relationship was correlational and not causational. After doing a secondary study, they actually discovered that the women given estrogen experienced decreased health as opposed to the control group who was not provided with the hormone. After comparing the two studies, it was discovered that the estrogen itself was not the key reason women were fighting illness better—there were other factors at play. This is why it is important to pinpoint whether two variables are simply varying together, or whether one has indeed an impact on the other.
In his book, Naked Statistics, Wheelan uses real-world examples to explain complex statistical theories. Not only do these case studies help us understand the world of stats—they show us why we should want to understand them. By applying these concepts to examples that are relevant to our lives, Wheelan "strips the dread out of data." The author of Yahoo!'s popular "Naked Economics" column and a lecturer on public policy at the University of Chicago, Wheelan is adept at sharing his work both in his writing and on stage. His talks on big data and statistics are not only extremely useful—they're fun too. He shows us that anyone can—and should—understand stats, and provides his audiences with the tools they need to make more informed decisions.