Model-Based Rankings, Estimates, Predictions and Classifications in Everyday Life
Milo Schield
W. M. Keck Statistical Literacy Project, Augsburg College, Minneapolis, MN, United States

We are exposed to rankings, estimates, predictions and classifications on a daily basis. Statistically-illiterate readers may not realize that these results are not observed facts but are inferences based on a process. They do not realize that the results may be strongly influenced by the assumptions involved. This paper investigates examples found in everyday life. Examples of rankings include those of cities and colleges. Examples of association-based estimates include the number of deaths attributed to factors such as second-hand smoke, obesity and radon. Examples of predictions include estimates of cost and time. Classifications include health (overweight, high cholesterol, high blood pressure) and personality (Myers-Briggs classification, being a morning or evening person). This talk investigates the sensitivity of these mode-based statistics to their underlying assumptions. Attention to assumptions should be an essential component of any statistical literacy course.

Keywords: Statistical literacy

Biography: Milo is the Director of the W. M. Keck Statistical Literacy Project, Vice President of the National Numeracy Network, US representative to the International Statistical Literacy Project and webmaster for He is a Professor of Business Administration at Augsburg College in Minneapolis, Minnesota where he teaches traditional statistics, statistical literacy for humanities majors and statistical literacy for business managers.