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300 Choosing default methodsdirections to study. A casual and introspective review suggests that there aremany different criteria we use to decide that a statistical method is worthyof routine use. Those of us who lean on particular ways of knowing (whichmight include performance on benchmark problems, success in new applications,insight into toy problems, optimality as shown by simulation studies ormathematical proofs, or success in the marketplace) should remain aware ofthe relevance of all these dimensions in the spread of default procedures.ReferencesAgresti, A. and Coull, B.A. (1998). Approximate is better than exact forinterval estimation of binomial proportions. The American Statistician,52:119–126.Basu, D. (1971). An essay on the logical foundations of survey sampling,part 1 (with discussion). In Foundations of Statistical Inference (V.P.Godambe and D.A. Sprott, Eds.). Holt, Reinhart and Winston, Toronto,pp. 203–242.Box, G.E.P. and Tiao, G.C. (1973). Bayesian Inference in Statistical Analysis.Wiley,NewYork.Dempster, A.P., Rubin, D.B., and Tsutakawa, R.K. (1981). Estimation incovariance components models. Journal of the American Statistical Association,76:341–353.Efron, B. (1979). Bootstrap methods: Another look at the jackknife. TheAnnals of Statistics, 7:1–26.Efron, B. and Morris, C. (1975). Data analysis using Stein’s estimator and itsgeneralizations. Journal of the American Statistical Association, 70:311–319.Gelman, A. (2010). Bayesian statistics then and now. Discussion of “Thefuture of indirect evidence,” by Bradley Efron. Statistical Science,25:162–165.Gelman, A., Meng, X.-L., and Stern, H.S. (1996). Posterior predictive assessmentof model fitness via realized discrepancies (with discussion). StatisticaSinica, 6:733–807.Gelman, A. and Robert, C.P. (2013). “Not only defended but also applied”:The perceived absurdity of Bayesian inference (with discussion). TheAmerican Statistician, 67(1):1–5.