This study analyzed the interference of sample size on Tukey’s test for non-additivity and found the sample size to optimize the test for soybean grain yield. Six experiments were conducted in a completely randomized block design with either 20 or 30 cultivars and three repetitions of each treatment. Grain yield was determined per plant, totaling 9,000 sampled plants. Next, sample scenarios up to 100 plants were simulated, estimating F statistic for a degree of freedom of the error in each scenario. After that, the optimal sample size was defined via power models and maximum curvature point. Results showed the number of sampled plants per experimental unit influences the estimates of Tukey’s test for non-additivity. Also, the sampling of 14 to 19 plants per experimental unit allows for maintaining the accuracy of the test.

This study analyzed the response of the Bartlett test as a function of sample size and to define the optimal sample size for the test with soybean grain yield data. Six experiments were conducted in a randomized block design with 20 or 30 cultivars and three repetitions. Grain yield was determined per plant, totaling 9,000 sampled plants. Next, sample scenarios of 1, 2, ..., 100 plants were simulated and the optimal sample size was defined via maximum curvature points. The increase in sampled plants per experimental unit favors Bartlett test’s precision. Also, the sampling of 17 to 20 plants per experimental unit is enough to maintain the accuracy of the test.

This study verified whether sample size would affect the precision of the analysis of variance in experiments with cauliflower seedlings. An experiment was carried out where the number of leaves and shoot, root and total length were measured. For each variable, resamplings with repositions were performed in sample scenarios of 1, 2, …, 100 seedlings per experimental unit, and the sample size was defined for the variance components through Schumacher models and maximum curvature points. The mean squares of the analysis of variance suffer direct interference from the number of sampled seedlings. The sampling of 16 seedlings per experimental unit is enough to estimate the analysis of variance reliably, promoting satisfactory precision gains compared to the sampling of only one seedling per experimental unit.