A modern introductory statistics course introduces students to the concepts underlying the design of statistical studies and to the reasoning needed to interpret the results of statistical analyses. The necessity of data for good decision-making, the value of sound data production, the omnipresence of variability in underlying processes, and the understanding and quantification of variability are central to statistical reasoning. Statistical thinking begins with a real-world problem and explores data to answer key questions. Statistical thinking recognizes the limitations of studies based on available data and the strengths of well-designed studies that produce data with the right information content. Statistical thinking questions the assumptions made by statistical models that explain, predict, and forecast—and it qualifies their use with measures of uncertainty. And statistical reasoning requires clear communication of results so that decision-makers can understand how they contribute to the problem solution.
Experiences with data and appropriate use of technology to support data analyses are essential to the success of a modern course.
In order to successfully develop a student's ability to think statistically, a teacher of an introductory statistics course must have deep knowledge of statistics as well as an appreciation of the differences between statistical thinking and mathematical thinking. Statistics teachers need to understand the ways that statisticians work with real data and approach problems, and to experience the joys of making discoveries using statistical reasoning. Anyone unfamiliar with the data-driven techniques used in modern introductory statistics courses should be mentored by an experienced statistics instructor.
Ideally, a department considering hiring or selecting someone to teach an introductory statistics course should require a candidate to have at least a master’s degree with a strong concentration in statistics. But because this is often not possible, the individual should have at a minimum at least the equivalent of
There is an active statistics education community and we strongly encourage all statistics instructors to make use of the many resources provided by the community. These include attending a workshop, minicourse or conference on teaching statistics, using available web resources, and reading articles from mathematicians and statisticians discussing key pedagogical differences between these fields. Information about resources and relevant professional development opportunities are provided in the PDF of the full statement, available below.
The ASA/MAA Joint Committee on Undergraduate Statistics encourages effective teaching in undergraduate statistical education, and our respective associations seek to be particularly helpful to those institutions where departments other than departments of statistics bear the primary responsibility for the teaching of statistics. Please direct questions or comments about these recommendations and resources to Ron Wasserstein, Executive Director, ASA, or Michael Pearson, Executive Director, MAA.