Literature Index

Gigerenzer has argued that it may be inappropriate to characterize some of the biases identified by Kahneman and Tversky as errors or fallacies, for three reasons: (a) according to frequentists, no norms are appropriate for single-case judgments because single-case probabilities are meaningless; (b) even if single-case probabilities make sense, they need not be governed by statistical norms because such norms are content-blind and can conflict with conversational norms; (c) conflicting statistical norms exist. I try to clear up certain misunderstandings that may have hindered progress in this debate. Gigerenzer's main point turns out to be far less extreme than the position of normative agnosticism attributed to him by Kahneman and Tversky: Gigerenzer is not denying that norms appropriate for single-case judgments exist, but is rather complaining that the existence and the nature of such norms have been dogmatically assumed by the heuristics and biases literature. In response to this complaint I argue that single-case probabilities (a) make sense and (b) are governed by probabilistic norms, and that (c) the existence of conflicting statistical norms may be less widespread and less damaging than Gigerenzer thinks.

Discusses some of the myths surrounding grading on the curve. Provides a simple explanation of such statistical terms as histograms, relative frequency, normal distribution, mean, and standard deviation. Describes how to restructure the curve, including the program listing for a computer program that will assist the teacher. (TW)

Many educators and researchers are trying to define statistical literacy for the 21st<br>century. Kimura, a Japanese science educator, has suggested that a key task of<br>statistical literacy is the ability to extract qualitative information from quantitative<br>information, and/or to create new information from qualitative and quantitative<br>information. This article presents research that offers a theoretical basis using the<br>SOLO Taxonomy to capture students' ability to create new information from<br>qualitative and quantitative information. This research shows that the "creation of<br>dimensionally new information" is a complex construct requiring further research<br>and a deeper analysis than Kimura appears to have used.

This article investigates two common methods of determining the line of best fit and then expands on the techniques used in these methods to find the line of best fit by graphing, estimating and using a microcomputer. (PK)