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Abstract
The practical merits of Neyman-Pearson confidence intervals versus hypothesis tests (especially p-values) are discussed. In the one-parameter case, confidence intervals convey much more comprehensible information, especially for the ultimate users of statistics. For more complicated inferences, p-values may be simpler and more routine.