Published online by Cambridge University Press: 01 January 2022
Experimenters sometimes insist that it is unwise to examine data before determining how to analyze them, as it creates the potential for biased results. I explore the rationale behind this methodological guideline from the standpoint of an error statistical theory of evidence, and I discuss a method of evaluating evidence in some contexts when this predesignation rule has been violated. I illustrate the problem of potential bias, and the method by which it may be addressed, with an example from the search for the top quark. A point in favor of the error statistical theory is its ability, demonstrated here, to explicate such methodological problems and suggest solutions, within the framework of an objective theory of evidence.
I am grateful to Douglas Allchin, Peter Lewis, Deborah Mayo, Cassandra Pinnick, and two anonymous referees for helpful comments on this paper, and to the participants in Deborah Mayo's 1999 NEH Summer Seminar at Virginia Tech for comments on an embryonic version. Earlier versions were presented at the University of Chicago and at PSA 2000 in Vancouver, B.C. My 1995 interviews with members of the CDF collaboration were assisted by a grant-in-aid from the American Institute of Physics.