Uncomfortable science is the term coined by
statistician John Tukey for cases in which there is a need to draw an
inference from a limited
sample of
data, where further
samples influenced by the same
cause system will not be available. More specifically, it involves the analysis of a finite natural phenomenon for which it is difficult to overcome the problem of using a common sample of
data for both
exploratory data analysis and
confirmatory data analysis. This leads to the danger of
systematic bias through
testing hypotheses suggested by the data.
A typical example is Bode's law, which provides a simple numerical rule for the distances of the planets in the solar system from the Sun. Once the rule has been derived, through the trial and error matching of various rules with the observed data (exploratory data analysis), there are not enough planets remaining for a rigorous and independent test of the hypothesis (confirmatory data analysis). We have exhausted the natural phenomena. The agreement between data and the numerical rule should be no surprise, as we have deliberately chosen the rule to match the data. If we are concerned about what Bode's law tells us about the cause system of planetary distribution then we demand confirmation which is not available, until and unless information about other planetary systems becomes available.
See also
Bibliography
- Diaconis, P. (1985) "Theories of data analysis: from magical thinking through classical statistics", inHoaglin, D.C et al. (eds) Exploring Data Tables Trends and Shapes. Wiley. ISBN 0-471-09776-4.
