Treatment 1 80-40 = 40
 Treatment 2 120-30 = 90
 Treatment 3 92-20 = 72
 Treatment 4 60-20 = 40
 Treatment 5 80-40 = 40
 Treatment 6 92-50 = 42

Treatments 2 and 3 have very large ranges, so the data
should be inspected further to find out why. Upon
inspection, the value 120 for Block III, Treatment 2 seems
too high, and the value 20 for Block I, Treatment 3 seems
too low. The researcher should look for specific physical
reasons why these numbers are unusual. Sometimes they can
be traced to copying or typographical errors or to some
unusual situation that occurred in a plot but did not
affect the other plots. If a specific reason not
associated with the experiment can be found, the numbers
may be replaced by new values obtained by checking
original field records, using missing plot formulas,
covariance, or other suitable methods.

Standardization of field data

 The field information taken directly from the
experimental plots (raw data) can seldom be utilized as
such for statistical analyses. Depending on the type of
crop, time of harvest, part of the plant of interest, and
many other factors, it is usually necessary to make some
numerical transformations that will provide more reliable
interpretations of the data. A common correction is made
when comparing yields of maize varieties with different
rates of maturity; if grain moisture is not standardized
to a uniform content, the excess moisture in the grain of
late-maturing varieties will cause an upward bias for
those varieties if direct plot weights are interpreted.
Also, plot size needs to be transformed in order to
produce more meaningful values. For example, it is better
to interpret tons, or kg/ha, of grain at a constant
moisture, than to consider just kilograms or grams per
plot, with no reference to the moisture content or plot
size. The correction procedures for these and other
factors are illustrated with field data.