Teaching statistics to medical students using problem-based learning: the Australian experience

Background: Problem-based learning (PBL) is gaining popularity as a teaching method in UK medical schools, but statistics and research methods are not being included in this teaching. There are great disadvantages in omitting statistics and research methods from the main teaching. PBL is well established in Australian medical schools. The Australian experience in teaching statistics and research methods in curricula based on problem-based learning may provide guidance for other countries, such as the UK, where this method is being introduced. Methods: All Australian medical schools using PBL were visited, with two exceptions. Teachers of statistics and medical education specialists were interviewed. For schools which were not visited, information was obtained by email. Results: No Australian medical school taught statistics and research methods in a totally integrated way, as part of general PBL teaching. In some schools, statistical material was integrated but taught separately, using different tutors. In one school, PBL was used only for 'public health' related subjects. In some, a parallel course using more traditional techniques was given alongside the PBL teaching of other material. This model was less successful than the others. Conclusions: There are several difficulties in implementing an integrated approach. However, not integrating is detrimental to statistics and research methods teaching, which is of particular concern in the age of evidence-based medicine. Some possible ways forward are suggested

Teaching Index Numbers to economists

Economic statistics are frequently reported in the form of index numbers. This article considers how the field of Index Numbers should be approached in the teaching of a general economic degree. While the topic finds a natural home in statistics modules, it is emphasised that the area can also be referred to in the teaching of other areas of economics. It is also emphasised that the differences between Index Numbers theory and the practice of compiling economic statistics such as inflation can help students gain a better understanding of applied economic statistics. Methods for assessing learning in the area are also considered and available material to support teaching is also summarised

Towards understanding models for statistical literacy: A literature review.

Despite statistical literacy being relatively new in statistics education research, it needs special attention as attempts are being made to enhance the teaching, learning and assessing of this strand. It is important that teachers are aware of the challenges of teaching this literacy. The growing importance of statistics in today's information world and conceptions and components of statistical literacy are outlined. Frameworks for developing statistical literacy from research literature are considered next. Strengths and weaknesses of the models are considered. Examples of tasks used in statistics education research are provided to explain the levels of thinking. The paper concludes with some implications for teaching and research

The Predictive Approach to Teaching Statistics.

Statistics is commonly taught as a set of techniques to aid in decision making, by extracting information from data. It is argued here that the underlying purpose, often implicit rather than explicit, of every statistical analysis is to establish a set of probability models which can be used to predict values of one or more variables. Such a model constitutes 'information' only in the sense, and to the extent, that it provides predictions of sufficient quality to be useful for decision making. The quality of the decision making is determined by the quality of the predictions, and hence by that of the models used. Using natural criteria, the 'best predictions' for nominal and numeric variables are respectively the mode and mean. For a nominal variable, the quality of a prediction is measured by the probability of error; for a numeric variable, it is specified using a prediction interval. Presenting statistical analysis in this way provides students with a clearer understanding of what a statistical analysis is, and its role in decision making.Statistics, teaching, prediction, probability model, prediction interval.

The Lancaster Postgraduate Statistics Centre CETL:building trust and statistical skills across disciplines

Statistics is often assumed to be a series of techniques. While it may be possible to teach postgraduate students generic techniques to enable them to carry out quantitative research, it is questionable how meaningfully this can be taught when separated from thinking about research data. Teaching students as close as possible to their own postgraduate degree scheme is one way forward, but this strategy presents teachers of statistics with new problems. Both students and departments have not necessarily understood statistics as a way of thinking - or understood it as a discipline with multiple and sometimes discordant approaches. The Lancaster CETL has allowed an opportunity to focus on teaching postgraduate statistics, yet in providing a focus for statistics attracts audiences from diverse interest groups. It presents us with the challenge to find a balance between what is possible generically and what needs to be specific