This section deals with interpreting statistical values and how to make sense of those measurements. Just briefly we will consider a few basic concepts in statistics which will lead to a more complete understanding of how to evaluate performance numbers and breeding values. In meat goat breeding we are concerned not only with performance values in individual goats, but also with the distributions of and relationships between values in a population.
Normal distribution
Values of individual animals, when viewed across an entire population, tend to follow a certain pattern or distribution. For example most of the phenotypic values for weaning weight in meat goats tend to be within 10 pounds of the average weaning weight for the population. Only about a third of all weaning weights are more than 10 pounds heavier or lighter than the population mean. Weaning weights more than 20 pounds heavier or lighter than the average are quite rare.
This pattern is typical of quantitative traits in general and is known as a normal distribution. This distribution appears graphically as a symmetric, bell-shaped curve.
Weaning weight (kg) of meat goat kids from Fort Valley State University, 2002/2003.
The horizontal axis represents levels of some value, e.g., phenotypic value or breeding value. The vertical axis represents the frequency of different levels of the value in the population. The area between the curve and the horizontal axis and bounded on each side by a given interval of values represents the proportion of observations in the population likely to be within that interval. For most quantitative traits most observations are near the mean value and relatively few observations occur at the “tails” of the distribution some distance from the mean. The reason quantitative traits are normally distributed is because they are affected by many genes. When the environmental effects are added to the mix, the number of classes and levels on the continuum increase substantially. For an understanding of the more important statistical measures that help us understand and describe normally distributed data, read through Appendix D.
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