I think this arises because statistics is more widely used by non-specialists than other areas of scientific method, and each group of non-specialists tends to develop its own terminology, often for mathematically similar methods. Sometimes, as in the case of the formulation of generalised linear models, it then takes substantial intellectual effort to demonstrate the equivalences.
Trevor Lambert
UK Medical Careers Research Group
Institute of Health Sciences
Oxford University
Old Road
Oxford OX3 7LF
Tel. 01865 226791
Fax 01865 226993
Website: http://www.uhce.ox.ac.uk/ukmcrg
>>> Philip McShane <[log in to unmask]> 10/10/02 10:22:06 >>>
This discussion of the meaning of 'Standard error' in relation to regression seems to me to highlight a serious problem in statistics: statisticians use different terms for the same thing, and the same term for different things, on an individualistic basis.
Consider for example the different forms of the Akaike Information Criterion, or phrases such as 'the Wilcoxon-Mann-Whitney test'. To say they are 'mathematically equivalent' or 'easily converted' is no excuse; would anyone say that centimetres and inches are mathematically equivalent and use them interchangably?
This does not happen so much in other areas of science: chemists know what 'propan-2-ol' is and zoologists what 'Drosophila melanogaster' is. They also know that if they use other terms (such as 'isopropanol') there is a danger of confusion or loss of precision. People even cope with changes, as long as these are agreed upon.
A bit more standardisation would be a good idea.
Regards
Phil
Phil McShane
Nuffield dept of Surgery
John Radcliffe Hosp
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