Uncomfortable science, as identified by statistician John Tukey,[1][2] comprises situations 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.
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).
If we are concerned about what Bode's law tells us about the cause system of planetary distribution then we demand confirmation that will not be available until better information about other planetary systems becomes available.