Literature Index

Over many years I have been attempting to improve statistical literacy in the population by changing the school curriculum. All such attempts have to be put in the general context of teaching, learning and assessing the subject. Ideally these should complement and reinforce each other. In practice they often conflict - in particular assessment can distort the learning process. In this talk I consider the nature of these conflicts and how they might be overcome in practice, giving examples from a lifetime's experience.

Missing data mechanisms, methods of handling missing data, and the potential impact of missing data on study results are usually not taught until graduate school. However, the appropriate handling of missing data is fundamental to biomedical research and should be introduced earlier on in a student's education. The Summer Institute for Training in Biostatistics (SIBS) provides practical experience to motivate trainees to pursue graduate training and biomedical research. Since 2010, SIBS Pittsburgh has demonstrated the feasibility of introducing missing data concepts to trainees in a small-group project-based setting that involves both simulation and data analysis. After learning about missing data mechanisms and statistical techniques, trainees apply what they have learned to a NIDDK/NIH-funded Hepatitis C treatment study, to examine how various hypothesized missing data patterns can affect results. A simulation is also used to examine the bias and precision of these methods under each missing data pattern. Our experience shows that under such project-based training, advanced topics, such as missing data, can be presented to trainees with limited statistical preparation, and ultimately, can further their statistical literacy and reasoning.

Multivariate data in ecological applications most often occur in the form of counts of species abundances in assemblages, where each species is a variable. These data do not generally conform to traditional statistical assumptions, and so special approaches and methods are needed in this context. Statisticians need to be informed about these special problems with ecological data. In addition, the rationale for complex experimental designs that is a trademark of most ecological studies needs to be well understood by applied statisticians in this area. On the other hand, successful approaches for teaching ecologists about the use of multivariate statistics include sticking to the conceptual, rather than the mathematical. I provide here an overview of the methods that have helped teaching across these two disciplines, including a general approach for the use of novel non-parametric methods in the analysis of ecological community data.

The influence of Japanese statisticians on the teaching of statistics has been insignificant; we still have no research and teaching institute such as a Department of Statistics in Japanese Universities. The following are examples of our efforts and indications of the improvement in the environment and the quality of statistical teaching in Japan.