For the data in Table I a visual inspection reveals no reason to doubt the assumptions. The only peculiarity of the data is the repetition of some values in the set of maize yields, but since no obvious explanation could be found the data were used for analysis and interpretation as shown in Table 2.

2.3 Comparisons of Treatment Means

Many sets of experimental results are wasted through an inadequate analysis of the results. In many cases this results from the use of multiple comparison tests of which the most prevalent, and therefore the one that causes most damage, is Duncan's multiple range test. The reason that multiple comparison tests lead to a failure to interpret experimental data properly is that such tests ignore the structure of experimental treatments and hence fail to provide answers to the questions that prompted the choice of experimental treatments.

Two particular situations in which multiple range tests should never be used are for factorial treatment structures and if the treatments are a sequence of quantitative levels. In the former the results should be interpreted through examination of main effects and interactions. In the second the use of regression to describe the pattern of response to varying the level of the quantitative factor should be obligatory. Thus, for the cowpea yield example, the effect of nitrogen on yield for cowpea variety B can best be summarized by the regression equation

         Yield = 591 - 1.77 N

where yield and N are both measured in kg/ha. The predicted yields for the four nitrogen levels (0, 40, 80, 120 kg/ha) are 591, 520, 449, and 379, which obviously agree very closely with the observed means.

Examples of the failure of experiments to interpret their data properly occur regularly in all agricultural research journals wherever multiple comparison methods are widely used. Examples of misuse and discussion of alternative forms of analysis are given by Bryan-Jones and Finney (1983), Morse and Thompson (1981), and many other authors. The only sensible rule to adopt when analyzing experimental data is never use multiple range tests or other multiple comparison methods.

2.4 Presentation of Results

The prime consideration in presenting experimental results should be to provide the reader with all necessary information for a proper interpretation of results, without unnecessary detail. This principle leads to some particular advice:

1    Tables of mean yields should always be accompanied by standard errors for differences between
     mean yields and the degrees of freedom for those standard errors.
2    When multiple levels of analysis are used, as for split plot designs then all the different standard
     errors must be given.
3    When the results are presented in graphic form the data should always be shown (plotting mean
     yields). A graph showing only a fitted line or curve deprives the reader of the opportunity to assess
     the reasonableness of the fitted model.