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The purpose of this article is to propose a simple semiparametric method for the construction of approximate confidence intervals for the mean of a distribution. The approach is motivated by the theory of estimating functions and is of particular value for problems involving small samples. Results from simulation studies suggest that in small samples this approach is somewhat more robust to non-normality than methods based on the Student's T-pivotal, and hence it compares favourably with standard methods in this context. [ABSTRACT FROM AUTHOR]

Over the past few years, the work of R.M. Royall and his colleagues has had a significant influence on the way in which many of statisticians think about sampling inference. This paper in particular is likely to become the focal point for a great deal of discussion, especially because of its highly suggestive empirical results. The author presents his comments to what other authors have said about the role of randomization in estimation. The Randomization Principle as formulated by other authors states that "the process of random sampling creates the only probability distribution on which reliable statistical inferences can be based."