Producers have adopted marketing strategies such as topping to help reduce economic losses from weight discounts, but they are still missing target weights and incurring discounts. We have previously determined the accuracy of sampling methods producers use to estimate the mean weight of the population. Although knowing the mean weight is important, understanding how much variation or dispersion exists in individual pig weights within a group can also enhance a producer’s ability to determine the optimal time to top pigs. In statistics and probability theory, the amount of variation in a population is represented by the standard deviation; therefore, our objective is to determine the sample size and method that is optimal for estimating the standard deviation of BW for a group of pigs in a barn.

Using a computer program developed in R (R Foundation for Statistical Computing, Vienna, Austria), we were able to generate 10,000 sample standard deviations for different sampling procedures on 3 different datasets. Using this program, we evaluated

weighing: (1) a completely random sample of 10 to 200 pigs from the barn, (2) an increasing number of pigs per pen from 1 to 15 pigs and increasing the number of pens until all pens in the barn had been sampled, and (3) selecting the heaviest and lightest pig (determined visually) in each pen and subtracting the lightest weight from the heaviest weight and dividing by 6. For all 3 datasets, increasing the sample size of a completely random sample from 10 to 200 pigs decreased the range between the upper and lower confidence intervals (CI) when estimating the standard deviation; however, this occurred at a diminishing rate. For the barn with the most variation, increasing the number of pens sampled while keeping constant the total number of pigs sampled led to a reduction in range between the upper and lower CI by 7, 6, and 31% for Datasets A, B, and C, respectively. Sampling method 3 resulted in a reduction of the range between the upper and lower CI from 9 to 62% for the 3 datasets. These data indicated that the distribution of pig weights can be practically estimated by weighing the heaviest and lightest pigs in 15 pens.