Sample-size needs for forestry herbicide trials

Abstract

Forest herbicide experiments are increasingly being designed to evaluate smaller treatment differences when comparing existing effective treatments, tank mix ratios, surfactants, and new low-rate products. The ability to detect small differences in efficacy is dependent upon the relationship among sample size, type I and II error probabilities, and the coefficients of variation of the efficacy data. The common sources of variation in efficacy measurements and design considerations for controlling variation are reviewed, while current shortcomings are clarified. A summary of selected trials estimates that coefficients of variation often range between 25 and 100%, making the number of observations necessary to detect small differences very large, especially when the power of the test (1–β) is considered. Very often the power of the test has been ignored when designing experiments because of the difficulty in calculating β. An available program for microcomputers is introduced that allows researchers to examine relationships among sample size, effect size, and coefficients of variation for specified designs, α and β. This program should aid investigators in planning studies that optimize experimental power to detect anticipated effect sizes within resource constraints.