title: Regaining confidence in confidence intervals for the mean treatment effect.
abstract: In many experiments, it is necessary to evaluate the effectiveness of a treatment by comparing the responses of two groups of subjects. This evaluation is often performed by using a confidence interval for the difference between the population means. To compute the limits of this confidence interval, researchers usually use the pooled t formulas, which are derived by assuming normally distributed errors. When the normality assumption does not seem reasonable, the researcher may have little confidence in the confidence interval because the actual one-sided coverage probability may not be close to the nominal coverage probability. This problem can be avoided by using the Robbins-Monro iterative search method to calculate the limits. One problem with this iterative procedure is that it is not clear when the procedure produces a sufficiently accurate estimate of a limit. In this paper, we describe a multiple search method that allows the user to specify the accuracy of the limits. We also give guidance concerning the number of iterations that would typically be needed to achieve a specified accuracy. This multiple iterative search method will produce limits for one-sided and two-sided confidence intervals that maintain their coverage probabilities with non-normal distributions.
authors.affiliation: Division of Statistics, Northern Illinois University, DeKalb, IL 60115, U.S.A.
authors.forename: Thomas W
authors.initials: TW
authors.lastname: O'Gorman
mesh_terms: D003198:Computer Simulation; D016001:Confidence Intervals; D006801:Humans; D012107:Research Design
keywords: Robbins-Monro process; permutation test; test inversion