Literature Index

This paper reports on an instrument designed to assess the practices and beliefs of instructors of introductory statistics courses across the disciplines. Funded by a grant from the National Science Foundation, this project developed, piloted, and gathered validity evidence for the Statistics Teaching Inventory (STI). The instrument consists of 50 items in six parts and is administered online. The development of the instrument and the gathering and analysis of validity evidence are described. Plans and suggestions for use of the STI are offered.

Why is training of conditional probabilities necessary? - Conditional probabilities play an important role within the statistics curriculum and in decision making under uncertainty (Bayesian inference). - People often have problems to use conditional probabilities in the right way (many examples in the literature of cognitive psychology). So it is important that - although there exist a lot of didactical suggestions - there has been no empirical research dealing especially with the improvement of the understanding of conditional probabilities. So we made a first try to close this gap.

Piaget worked out his logical theory of cognitive development, Koehler the Gestalt laws of perception, Pavlov the principles of classical conditioning, Skinner those of operant conditioning, and Bartlett his theory of remembering and schemata - all without rejecting null hypotheses. But, by the time I took my first course in psychology at the University of Munich in 1969, null hypothesis tests were presented as the indispensable tool, as the sine qua non of scientific search. Post-World War 2 German psychology mimicked a revolution of research practice that had occurred between 1940 and 1955 in American psychology. What I learned in my courses and textbooks about the logic of scientific inference was not without a touch of morality, a scientific version of the 10 commandments: Thou shalt not draw inferences from a nonsignificant result. Thou shalt always specify the level of significance before the experiment; those who specify it afterward (by rounding up obtained p values) are cheating. Thou shalt always design the experiments so that thou canst perform significance testing.

The necessity for stimulating the interest of pupils in mathematics in general and statistics in particular is made clear by the results of surveys. The studies showed, that pupils are tired of mathematics. Mathematics is generally regarded as one of the most unpopular subjects at school. The pupils don't achieve so well in this subject . The causes might originate in several variables. The spectrum of possible causes for these bad results stretches from genetic disposition to deficits in learning behaviour. One of the aspect is that mathematics/statistics use the symbol language. And just exactly this language is what we want to look at more closely. As an essential feature of symbols the fact must be emphasized that they "all mean something other than themselves, that they all point to something besides themselves".