Teaching Introductory Statistics
In this talk, we will discuss the generally accepted set of recommendations for introductory statistics classes. Following a 1992 paper by Cobb, we will argue that an introductory statistics course should be designed to emphasize statistical thinking over routine computations and to make students realize that
• Decisions must be based on evidence (data); it is dangerous to act on assumptions not supported by evidence
• It is difficult and time-consuming to formulate problems and to get data of good quality
• Variability is ubiquitous; it is the essence of statistics
• Variability can be measured and quantified by considering randomness and distributions, fit and residual, mathematical models for patterns, and diagnostics.
We will conclude our talk by introducing the 2003 research funded by the American Statistical Association (ASA), namely, the Guidelines for Assessment and Instruction in Statistics Education (GAISE) for the purpose of developing both K-12 and introductory college courses.
A/V requirements: Laptop, LCD Projector