In social policy evaluations, the multiple testing problem occurs due to the many hypothesis tests typically conducted across multiple outcomes and subgroups, which can lead to spurious impact findings. This article discusses a framework that balances Types I and II errors for addressing this...

A well-known problem in multiple regression is that it is possible to reject the hypothesis that all slope parameters are equal to zero, yet when applying the usual Student's T-test to the individual parameters, no significant differences are found. An alternative strategy is to estimate...

In this, article we consider a Bayesian approach to the problem of ranking the means of normal distributed populations, which is a common problem in the biological sciences. We use a decision-theoretic approach with a straightforward loss function to determine a set of candidate rankings. This...

Statistical procedures that correct for multiple testing typically result in hypothesis tests with reduced statistical power because adjustment methods reduce the likelihood of identifying real differences between contrasted groups. There is disagreement among researchers about the use of...

The level of differential gene expression may be defined as a fold change, a frequency of upregulation, or some other measure of the degree or extent of a difference in expression across groups of interest. On the basis of expression data for hundreds or thousands of genes, inferring which genes...

Exact analytic expressions are developed for the average power of the Benjamini and Hochberg false discovery control procedure. The result is based on explicit computation of the joint probability distribution of the total number of rejections and the number of false rejections, and expressed in...

Consider a probability model P θ,α , where θ=(θ 1 ,θ 2 ,…,θ k ) T is a parameter vector of interest, and α is some nuisance parameter. The problem of testing null hypothesis H 0 : θ 1 =θ 2 =…=θ k against selecting one of k alternative hypotheses H i :θ i =θ [ k ] θ [1] , i...

Case-control studies of genetic polymorphisms and gene-environment interactions are reporting large numbers of statistically significant associations, many of which are likely to be spurious. This problem reflects the low prior probability that any one null hypothesis is false, and the large...

For situations where the number of tested hypotheses is increasingly large, the power to detect statistically significant multiple treatment effects decreases. As is the case with microarray technology, often researchers are interested in identifying differentially expressed genes for more than...