Models…

Abstract

The year 2016 was, especially for people who professionally deal with statistics, a very interesting year. Especially two events caused more than a bit of turmoil in our profession: the Brexit referendum and the recent US presidential elections. The most interesting observation was the response of the media “the day after the night before”: “this outcome was considered extremely unlikely even in the best models available”. People, in both cases, just showed a different behavior than that what was included in these models. This is an issue I have addressed also in earlier editorials in this journal: in many papers that are published in this journal (and in even more papers that do not make it to publication), often very sophisticated models are used as part of a prediction and/or optimization process without even remotely addressing possible underlying effects that can totally change the predicted outcome. As the two examples that were mentioned have illustrated, even models that have been used for decades can lose their validity because of, earlier unknown, mechanisms that can start play a role at unexpected/unanticipated moments. In teaching statistics and, especially, data analytics we are in our university therefore reconsidering our education methods in this field. The idea is to spend less time on the statistical details of various models but to teach students especially to look into the underlying mechanisms that determine a model. Are there (new) trends that can be of relevance and are these trends taken into account? Are there, especially, outliers in datasets that may require a more detailed analysis? Are there systematic/systemic effects that may be of relevance and how are these effects covered in the available models? The focus is here to teach students more to the extreme data points and their underlying mechanisms than to the “goodness of fit” of a given model to the majority of a set of data points. This is, at our university, not only new for students but often also for the lecturers involved. For many of them, it has been quite a while since they went out into the field looking into underlying phenomena instead of looking into clouds of data points on a computer screen. I do not say that with this approach we would have been able to better predict the outcome of the two stated events. But, at least, the outcome could have come with less surprises….