In today’s world we are surrounded by predictions. For instance, during political elections the main focus of the media and the public is not on the differences between the candidates’ positions, but rather on the “horse race” aspect of the competition. Issues at stake are secondary compared to the main question: who is going to win?
Professional pollsters bombard us with predictions, all of them carrying footnotes about stratified sampling, confidence values and error margins. Unfortunately, for most people, math classes are not the fondest memory from school years, and for many, even remembering math exams still brings a sort of physical discomfort. It is not surprising that all the “details” about statistics are often overlooked.
However, it is important that we pay more attention to the details of the statistics we are being shown. For instance, do you see anything wrong with this chart from late 2011? (Hint: compare March and November).
Problems with statistics and predictions are not limited to graphic representation and in fact, can be more complicated and challenging, especially with the advent of Big Data and its use in making projections.