|Eric Sowey (Australia)|
"Data are not information, information is not knowledge, knowledge is not wisdom". This aphorism is popular with statisticians and information technologists alike. But its unremitting negativity hardly points the way to good practice. That demands an accent on the positive. What will turn data into information? What will turn information into knowledge? What will turn knowledge into wisdom? The first two of these questions are at the core of every university course in statistics. They provide the motivational foundations for learning statistical description and statistical inference, respectively. However, it is the third question that sets the keystone to a sturdy practical statistics curriculum.
Whose wisdom is at issue here? This paper takes the view that today it is important for a professional statistics education to show the practising statistician the way not only to his/her own wisdom, but to his/her client's wisdom, as well. Essential to the getting of wisdom in statistical matters is a competence to argue back to a statistic or to a statistical inference. Such a competence represents functional numeracy of a high order. So central is the role of statistical evidence and argument in many controversial decision contexts, which have profound social implications, that it is no longer adequate in a democratic society for a statistician's conclusions to be accepted uncritically by an uncomprehending lay community.
How, then, is the present alarmingly low level of community numeracy to be enhanced? 'Statistical literacy' programs in schools and adult education centres are one way to go. But, in the face of competing attractions yielding more immediate gratification, their broad appeal is doubtful. Moreover, the rate of diffusion through the community of what is taught is likely to be low. More promising is a focus on enhancing the numeracy of the statistician's client. The client generally has the motivation to understand both the strengths and the weaknesses of the statistician's conclusions, as do those who may seek to oppose those conclusions and their implications for policy. For particularly divisive social issues, the rate of diffusion in the community of a numerate understanding of the issues may be expected to be correspondingly high. No one, this paper argues, is better placed to enhance the client's numeracy than the statistician him/herself.
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