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J.O. Berger 265How does a frequentist know when a serious conditioning mistake is beingmade? We have seen a number of situations where it is clear but, in general,there is only one way to identify if conditioning is an issue — Bayesian analysis.If one can find a Bayesian analysis for a reasonable prior that yields the sameanswer as the frequentist analysis, then there is probably not a conditioningissue; otherwise, the conflicting answers are probably due to the need forconditioning on the frequentist side.The most problematic situations (and unfortunately there are many) arethose for which there exists an apparently sensible unconditional frequentistanalysis but Bayesian analysis is unavailable or too difficult to implement givenavailable resources. There is then not much choice but to use the unconditionalfrequentist analysis, but one might be doing something silly because of notbeing able to condition and one will not know. The situation is somewhatcomparable to seeing the report of a Bayesian analysis but not having accessto the prior distribution.While I have enjoyed reminiscing about conditioning, I remain as perplexedtoday as 35 years ago when I first learned about the issue; why do we still nottreat conditioning as one of the most central issues in statistics?ReferencesBarnard, G.A. (1947). A review of ‘Sequential Analysis’ by Abraham Wald.Journal of the American Statistical Association, 42:658–669.Berger, J.O. (1985). Statistical Decision Theory and Bayesian Analysis.Springer, New York.Berger, J.O. and Berry, D.A. (1988). The relevance of stopping rules in statisticalinference. In Statistical Decision Theory and Related Topics IV,1. Springer,NewYork,pp.29–47.Berger, J.O., Boukai, B., and Wang, Y. (1999). Simultaneous Bayesianfrequentistsequential testing of nested hypotheses. Biometrika, 86:79–92.Berger, J.O., Brown, L.D., and Wolpert, R.L. (1994). A unified conditionalfrequentist and Bayesian test for fixed and sequential simple hypothesistesting. The Annals of Statistics, 22:1787–1807.Berger, J.O. and Wolpert, R.L. (1984). The Likelihood Principle. IMSLectureNotes, Monograph Series, 6. Institute of Mathematical Statistics,Hayward, CA.Berkson, J. (1938). Some difficulties of interpretation encountered in theapplication of the chi-square test. Journal of the American StatisticalAssociation, 33:526–536.