The coefficient of variation (CV) has been used for many years by rese
archers to determine the validity of performance trials. The premise b
ehind the CV, that the standard deviation is proportional to the mean,
compromises one of the assumptions of a normal distribution (i.e., th
at the sample mean and sample variance are independent), If there is a
nonnormal distribution, then the data may need to be transformed, whi
ch in turn may invalidate the use of the CV. A constant CV across tria
ls implies a relationship between the error variance and the mean such
that the slope = 2.0 in the regression of In error variance on In mea
n. The purpose of this paper is to stress the relationship between the
error variance and mean in the CV, review this relationship in actual
yield data, and examine the effects of various transformations on CVs
and coefficient of determination (R-2), The In error variance regress
ed on In mean for several agronomic crops ranged from -0.11 for full-s
eason corn (Zea mays L.) to 1.31 for oat (A vena sativa L.). Although
some crops had a nonzero regression coefficient for this relationship,
none approached 2.0, the level that supports the hypothesis that the
CV is a viable tool for comparing the relative variation of different
trials. Data transformations (e.g., square root, logarithmic, angular,
inverse, reverse, and addition of a constant) tend to lower CV values
in most cases, but can cause dramatic increases, depending on the nat
ure of the variance and the specific transformation. On the other hand
, R-2, which is a measure of the amount of variability accounted for i
n the model, remains relatively unaffected by most transformations. Re
asonable R-2 values for determining validity of performance trials may
vary by location and crop species, Examination of North Carolina data
revealed that discarded trials generally had R-2 values less than 50%
. The R-2 may be affected by the size of the dataset, and so adjusted
R-2 maybe more useful for comparing trials of varying sizes; however,
adjusted R-2 will tend to be larger where there are larger differences
among entries. There is no one perfect measure of the validity of tri
al data, but the R-2 and the adjusted R-2 are reasonable alternatives
to the CV and should be examined along with other statistical measures
when evaluating crop performance data.