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In the frequentist approach to inference, statistical procedures are assessed by their performance over a hypothetical long run of repetitions of a process deemed to have generated the data.

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I think you are doing 3 mistakes: 1) in the frequentist example, you treat the data as if they were on the "normal" (logarithm) scale, while in the bayesian example, you treat them as on the … "lognormal" scale. Supposing the data are on the lognormal scale, you should probably modify your frequentist example to something like: > data1 = c(0.32618457, 0.29166954, 0.27427996, 0.23844847 …
answered Jan 17 '13 by Curious