Statistics for the Terrified: teachers

A typical teaching and learning experience might be as follows:

In a classroom session devoted to the technique of regression (or linefitting) the teacher will use the software to graphically demonstrate the purpose and uses of regression. The tutorial contains animated examples for the class to see.

The three statistical summaries by which the 'fit' of the line to the data is described are then introduced by the teacher (gradient/slope, intercept and correlation). The students can spend a period of time exploring these, perhaps five or ten minutes, using the interactive graph. Data is changed by dragging points on their screens, and they can then observe how the visual fit of the line changes and the effect on the three values. This provides the beginnings of an intuitive understanding of the relationships between the data, the line of best fit, and these three key values.

The teacher then moves the students on to a series of exercises or 'datagames'. The tutorial provides randomly generated data sets in the interactive graph, and the students are challenged to change the data in order to reach randomly generated target values for each of the gradient, intercept and correlation in turn.

These exercises have been found to be a very powerful method of giving users a firm grasp of what these values actually represent. For example, you can get a high correlation with a particular slope - and then a low correlation with exactly the same slope. Slope and correlation are not the same thing; it is obvious now. After this, more difficult but realistic exercises may be used in which the target is a combination of values, again generated randomly.

After formal teaching sessions, these exercises are available to the individual student for consolidation of their learning, either on the institutional network or via the single-user version.

Because the targets of all these exercises are different each time, the user cannot learn to succeed in them without gaining some real understanding. Because the course was developed by a statistics teacher, it is designed as an all-round tool which hugely reduces the teacher's overhead in session planning and exercise development.

By providing the foundations of statistical understanding in a resource which can be accessed outside contact time, the teacher doesn't have to explain the mathematics, and is able to concentrate instead on the applications of statistics in their own field. Statistics for the Terrified is good for the teacher because it makes the student want to learn: it is interactive, immediate, fun and yet sophisticated. It provides a sense of real achievement and a true understanding of the mechanisms of research.

Remember how you learned to ride a bicycle: although you were dealing with friction, angular velocity, momentum, brake force, gravity and other heavy concepts, you learned how they acted by just doing it. The initial experience of Statistics for the Terrified works in exactly this way, providing a context for recognition of the principles when they are explained in more detail.

Learning with Statistics for the Terrified helps students because it reformulates the presentation of ideas away from the traditional mathematical approach and into straightforward puzzles and pattern recognition. Pattern recognition is our most basic and most practised learning skill, and there are few prerequisites, as in childhood. The mathematical baggage is put to one side.

This tutorial is ideal for those teaching students who require critical appraisal skills, and will also enable those undertaking research projects not only to select the best techniques, but also - with the new module on analysing your own data - to perform the most common statistical tests. Should they need to understand more sophisticated techniques, they will have gained insight which will carry them forward.

Statistics for the Terrified meets its objectives by enabling a practical and intuitive understanding of the basic purposes and techniques employed in statistics, and by providing a simple analysis tool for project data.

Students are able to explore 'what if' scenarios using interactive graphics rather than complex mathematics (which would require explanation in themselves), and these provide a commonsense intuitive context within which more formal teaching can flourish. These scenarios also help the student to understand the principles by exploring what happens to the statistical outcomes if the data is changed, rather than learning an example; which can all too easily be the outcome of traditional teaching where the data is sacrosanct.

The Analysis module allows users to input their own data, thus avoiding the need for statistics software such as SAS, SPSS, Minitab etc. at this level, and removing a heavy burden from teachers and support services.

A key design principle has been that the software is easy to navigate. A user who wants to refer back or forward is typically able to move to any other part of the tutorial in two or three mouseclicks.

Students are supported by the provision of a glossary. The definition of any statistical term can be obtained by double-clicking the word where it appears.