SAMPLE-SIZE NEEDS FOR FORESTRY HERBICIDE TRIALS

Forest herbicide experiments are increasingly being designed to evalua te smaller treatment differences when comparing existing effective tre atments, 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 source s of variation in efficacy measurements and design considerations for controlling variation are reviewed, while current shortcomings are cla rified. A summary of selected trials estimates that coefficients of va riation often range between 25 and 100%, making the number of observat ions necessary to detect small differences very large, especially when the power of the test (1 - beta) is considered. Very often the power of the test has been ignored when designing experiments because of the difficulty in calculating beta. An available program for microcompute rs is introduced that allows researchers to examine relationships amon g sample size, effect size, and coefficients of variation for specifie d designs, alpha and beta. This program should aid investigators in pl anning studies that optimize experimental power to detect anticipated effect sizes within resource constraints.