On the Hedges Correction for a t-Test

When cluster randomized experiments are analyzed as if units were independent, test statistics for treatment effects can be anticonservative. Hedges proposed a correction for such tests by scaling them to control their Type I error rate. This article generalizes the Hedges correction from a posttest-only experimental design to more common designs used in practice. We show that for many experimental designs, the generalized correction controls its Type I error while the Hedges correction does not. The generalized correction, however, necessarily has low power due to its control of the Type I error. Our results imply that using the Hedges correction as prescribed, for example, by the What Works Clearinghouse can lead to incorrect inferences and has important implications for evidence-based education.