Subject: Re: EPR Approach to Intro Stat: Relationships Between Variables Date: July 18, 1996 2248 EDT From: "Donald Macnaughton" <donmac@matstat.com> (formerly donmac@hookup.net) To: edstat-l@jse.stat.ncsu.edu, bwgriffin@gsvms2.cc.gasou.edu
On July 16, 1996 Brian Griffin wrote > The emphasis [in the EPR approach] is clearly placed on pre- > diction and control, yet I think the most important goal of > research is totally ignored in your discussion--explanation. I agree that explanation (sometimes referred to as "understand- ing" or sometimes linked to the concept of "understanding") is an important goal for many *researchers*. However, I don't think that we should concentrate on the goals of *researchers*. Instead, I think we should concentrate on the goals of empirical research *for society*. We should concentrate on the goals for society because if re- searchers are working to satisfy societal goals (which here in- clude commercial goals), the researchers are more likely to get funded, and their work is more likely to make a useful social contribution. Thus Brian's posting leads to the following important question: Which of the following goals of empirical research are more important *for society*: - prediction (including control) or - explanation (including understanding)? Consider a thought experiment: a. Suppose you are offered a technique that yields substantially more accurate predictions (or controls) in some area of life than are now available. But suppose the technique provides *absolutely* no explanation or understanding of how the pre- dictions work or how the phenomena under study work. If you can use the predictions to your benefit, you will probably be very interested in using them even though you don't understand how they work. (Of course, most people would prefer that the prediction ability be *accompanied* by explanations and under- standing because the explanations and understanding would make us more comfortable that the predictions are reliable. But if we can't have the explanations and understanding, so be it--we will still be pleased to take advantage of the predictions.) Thus accurate predictions (or controls) have high societal value, regardless of whether they are accompanied by explana- tions or understanding. b. Suppose you somehow know that certain explanations (or under- standing), although correct about the past, contain *abso- lutely* no information relevant to the prediction or control of variables in the future. (Thus the explanations would ap- pear to be of no practical use.) Most practical people (and most funding agencies) will be uninterested in these explana- tions and will be unwilling to support efforts to obtain fur- ther such explanations. That is, explanations (or understand- ing) with no hope of future prediction or control have low so- cietal value. Since accurate predictions have high societal value *regardless* of whether they are accompanied by explanations, but correct ex- planations have low societal value *unless* they are accompanied by accurate predictions (or hope of predictions), therefore pre- diction (control) is more important to society than explanation (understanding). Therefore, it is reasonable to emphasize prediction and control of the values of variables in the introductory statistics course. Approaching from a different point of view, note that for most scientists the acid test of whether someone has fully "explained" something is whether that person can accurately *predict* the values of relevant variables. This suggests that the essence of an explanation lies in its ability to make accurate predictions (or enable accurate control). Finally, it's important to note that explanations and understand- ing are sometimes very helpful as middle steps in discovering how to predict or control the values of variables. So I'm not saying that explanation and understanding are unimportant. However, in view of the arguments above, I maintain that, from the point of view of society, the goals of explanation and understanding are subordinate to the goals of prediction and control. LINK Material about the entity-property-relationship approach to the introductory statistics course is available at http://www.matstat.com/teach/ ----------------------------------------------------------- Donald B. Macnaughton MatStat Research Consulting Inc. donmac@matstat.com Toronto, Canada -----------------------------------------------------------