John Kruschke’s career as an experimental psychologist took an unexpected turn when he began to question traditional statistical methods
If you think statistics is dull and dry, and the math outright intimidating – well, you may have a point. It can be intimidating. As for dull and dry? Think again.
Between scientists and their findings, questions and answers, data and the trends within it, is the science of statistics. Statistics, Kruschke explains, helps you detect the signal in that “heap of noisy numbers. You need statistics to find out what kind of trend you can describe in this heap of numbers and how big it is relative to the noise.”
Does a new drug have a benefit? Will caffeine boost your IQ? Does chocolate prevent heart disease? Red wine?
The science of statistics, as Professor John Kruschke describes it, provides the most critical plot twists in the detective story at the core of science itself.
Kruschke’s close encounter with statistics began almost 30 years ago when he started teaching the subject to first-year graduate students. He began the stint, like most statistics professors, teaching the traditional “frequentist” approach. The more he taught this approach, however, the more he began to question it. As he notes in a short bio, he reached a point where he “could no longer teach corrections for multiple comparisons with a clear conscience. The perils of p values provoked him to find a better way.”
Ultimately, his career took what he calls “a complete digression,” when he adopted an alternative statistical approach – Bayesian statistics – that was just then gaining currency. But it was less a digression perhaps, than an all-out conversion experience, one that was also taking place more broadly among statisticians and scientists of many kinds, who were turning to Bayesian statistics as an alternative to more traditional, and potentially problematic, statistical methods.
Kruschke himself has since become a leading proponent of Bayesian statistics, which he has enthusiastically shared and continues to share with his many students, readers, and audiences around the world. He has produced numerous articles on the topic and two editions of a major textbook, Doing Bayesian Data Analysis. He has taught scores of workshops for groups with varied professional and academic leanings, from the Federal Aviation Association, the Food and Drug Administration, to medical economists in Norway, health scientists in Scotland, and many universities across the United States and Europe. Most recently, he was an editor for a special volume of essays devoted to Bayesian statistics in the Psychonomic Bulletin and Review, for which he also wrote two essays intended for an audience of Bayesian “newcomers.” All of these efforts have helped bridge the gap between statisticians, who develop statistical techniques, and others who use statistical techniques in their work.
Named for its eighteenth-century originator Reverend Thomas Bayes, Bayesian statistics had remained in the shadows of the traditional, institutionally entrenched “frequentist” method. Then in the 1990’s and 2000’s computational advances, a collapsing wall of philosophical resistance with respect to features such as making use of prior possibilities – and researchers like Kruschke – brought it further into the daylight.