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International Journal of Epidemiology 2003;32:687-691
© International Epidemiological Association 2003

Reprints and Reflections

Tests of significance considered as evidence*

Joseph Berkson, MD

Division Of Biometry and Medical Statistics, Mayo Clinic

The first 150 words of the full text of this article appear below.

‘After all, the higher statistics are only common sense reduced to numerical appreciation.’—Karl Pearson

There was a time when we did not talk about tests of significance; we simply did them. We tested whether certain quantities were significant in the light of their standard errors, without inquiring as to just what was involved in the procedure, or attempting to generalize it. In recent years tests of significance have been more broadly conceived as tests of hypotheses, and they have been generalized as t tests, F tests and certain amplifications of these, such as analysis of variance or of covariance. It is hardly an exaggeration to say that statistics, as it is taught at present in the dominant school, consists almost entirely of tests of significance, though not always presented as such, some comparatively simple and forthright, others elaborate and abstruse. Behind this is a doctrine of analysis that consists of . . . [Full Text of this Article]


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