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Statistical Literacy and Liberal Education at Augsburg College

Statistical literacy is critical thinking about arguments that use statistics as evidence. Statistical literacy Focuses primarily on inductive reasoning and strength of argument for a disputable claim. In observational studies, statistics are contextual; they depend on what is taken into account. This locus on context is why statistical literacy can be viewed as an essential component of a liberal education. And given the role of observationally-based statistics in modern life (e.g., public policy issues, school quality, health choices), statistical literacy should be an essential component of education for responsible citizenship and effective personal decision making. According to AACU it involves the statistical approach in focusing on the role of context, conditional reasoning, and variation. Rut statistical literacy goes beyond quantitative literacy or numeracy by focusing on the ability to read, to interpret, and to communicate. Numeracy focuses primarily on numbers; statistical literacy focuses more on the words framing the numbers. The primary goal of the W. M. Keck Statistical Literacy Project at Augsburg College is to develop statistical literacy as an interdisciplinary curriculum in the liberal arts. William Frame, president of the college, observed, "Augsburg has a long history of uniting the liberal and the practical to prepare students as citizens and stewards of the world. This concept [statistical literacy] is central to our effort in behalf of interdisciplinarity." And indeed, statistical literacy is a key component of a practical liberal arts education. As Augsburg's director of general education, Professor Joan Griffin, has noted, "statistical literacy, with its strong focus on arguments involving statistics in public policy, helps students become better citizens and leaders." Numbers and Words In the statistical literacy course at Augsburg College, students learn to distinguish the truth of the statistic from the strength it gives in an argument. One example involves two hunters being chased by a bear (Friedman 1997). The first hunter says, "We're doomed. This bear can run faster than we can." The second hunter says, "No! I don't have to outrun the bear; I just have to outrun you." The truth of the statistic (run faster) is independent of its support for the truth of the claim. By focusing on grammar to distinguish association from causation, students reflect on differences between statements like the following: "People who are heavier tend to be taller," "as weight increases, height tends to increase," "increasing weight tends to increase height," and "if people gain weight they can expect to become taller." The first asserts association; the last asserts causation. To social scientists the middle two assert association, but to most readers they assert causation. Students also locus on the definitions of soft terms such as "bullying." What is it? Can "bullying" be defined to make the percentage of students who are involved in bullying a larger or smaller number? Students quickly learn that small changes in syntax can produce large changes in semantics. In describing ratios, they learn that "the percentage of women who are runners" is different from "the percentage of women among runners." In comparing ratios, they learn that "widows are more likely among suicides than are widowers" is quite different from "widows are more likely to commit suicide than are widowers. …