A Probability Inequality and Its Applications in Selecting Best Normal Means.

In an experiment of treatment selections, random samples are drawn from k populations with different means. The probability that, among the samples, the sample mean from the population with the highest underlying expected value turns out to be the highest is called the probability of correct selection (PCS). In this article, a probability inequality motivated by the PCS problem, is obtained with respect to change in variances. As an application of the inequality in normal mean selection, it is shown that in a multiple treatment experiment setting, by increasing sample sizes, and thus obtaining a higher precision in the estimation of means, in general (but not always), one will have a higher PCS. However, there are special case exceptions. [ABSTRACT FROM AUTHOR]