Agricultural research involves the comparing of two or more variables or treatments, imposed on a sample of the population of inference. This comparison is done in order to determine differences due to respective treatments. The proper experimental design will remove as many other sources of variation as possible. The main objective is to explore improved methods for producing crops and/or livestock in order to maximize economic return to the farm.
Agricultural researchers use statistics as a tool to help differentiate between variables so real or meaningful conclusions can be drawn from a relatively large amount of data. Statistics give us a measure of variation in the experiment, included in many tables as the Std Err or standard error. This value represents the variation of each experimental unit from the mean of the group. The experimental unit is the lowest common grouping to which treatments are applied. In most cases for livestock research, this is a pen of animals. Tradeoffs are required when limited numbers of pens are available, as more treatments will reduce the potential number of replicates. P values are provided in many of the tables. These values suggest the probability of the observed result occurring due to treatment, such as a P value of .05 indicates that this result is likely to occur due to random effect only 5% of the time. Conversely, the observed effect will occur due to the treatment imposed 95 percent of the time. Obviously, the lower the P value, the greater the confidence in the results of the study. Differences in treatments are reported in the tables by inserting different letters as superscripts next to the respective values.
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this publication could not be accomplished without the capable assistance of
Dale Burr and Tim Schroeder, full time livestock technicians. Seasonal assistance was also provided by Nicole
Wolkenhauer, Chris Kubal, Jacee Lund, Jared Higgins and Rick Richter, with occasional
but timely help from a few crops oriented staff. Thank you all for your dedicated service to
the livestock industry in