Determination of minimum number of animals in comparing treatment means by power analysis

Objective. The purpose of this study was to determine the minimum number of animals (minimum sample size) in treatment comparisons with different effect sizes (0.25-2.0), the number of treatments (2-7), and the power of the test (80-95%). In addition, linear, quadratic, and cubic regressions equations that estimate the minimum sample size that should be used in treatment comparisons were developed. Materials and methods. Within the scope of this research, average daily gain (GDP) of feedlot cattle experiments conducted at Iowa State University totaling 1283 steers were used. The power of the test was calculated after random samples were taken from the GDP data and the differences between the treatments in terms of standard deviation were established. This process was iterated 1000 times via a macro written in the Minitab package program in the number of treatments and power levels to be compared. Results. It was found that the cubic regression equations gave more reliable results than others. As a result, after determining the number of treatments, the power of the test, and the effect size, a sufficient number of experimental units can be easily determined by using the estimation equations created without power analysis. Conclusions. In this way, excess money expenditure and financial loss in scientific studies can be prevented and the opportunity to find financing more easily can be provided.