What is the difference between duncan and tukey test

The latter can be under global null hypothesis or partial null hypothesis, or maximum experimentwise error rate MEER which is the preferred one. Conagin ; Conagin et al. The aim of this study is to propose two modifications for the Tukey test and to evaluate the power and the efficiency of the 11 classical and five modified multiple comparison tests. All classical tests were calculated using the SAS software. The evaluation of the power of each test was made by the value of the percentage of the number of significative differences obtained in relation to the number of experiments performed.

A brief description of the modifications of the Tukey test is presented. The differences between means larger than this lsd value will be declared statistically significative according to the TuM 1 test. The differences between means larger than this lsd value will be declared statistically significative according to the TuM 2 test.

Nevertheless, two treatments that are consecutive in the ordered set, due to the size of experimental error or smaller number of replications or other causes, have generally not significative differences. When the real value of the differences decreases, the power of each test decreases and the difference of power among the different tests increases.

The efficiency of each test calculated in relation to the unilateral Student t test is shown in Table 2. This is very important because in breeding programs and other types of research the new aimed progress always tends to be more difficult to be obtained and the progress is smaller.

If he wants an error a for the global H 0 or H' 0 , then he must apply an experimentwise type of test. The values shown in Table 1 may help in his choice. When two means are compared, the software used generally gives the exact probability p of the test; the result helps to evaluate better the degree of confidence of the obtained result. In this case they are the most efficient test of all experimentwise MEER types.

Table 3. Abrir menu Brasil. Scientia Agricola. Nevertheless this misuse can still be found in the literature. But a much commoner problem is the use a multiple comparison test for comparing ordered means - in such situations a test for trend or regression analysis is much more informative and powerful.

There is also confusion about which measure of location to report especially following a transformation. In general, detransformed means should be presented rather than the original untransformed means. A further issue is that most researchers only use pairwise tests. Orthogonal contrasts which include combined mean comparisons are often more appropriate. We give examples such as comparing shrimps in two habitat types where the latter approach would have been both more informative and more powerful.

The other misuses of multiple comparison tests are to use excessively liberal tests or excessively conservative tests. In the first category comes Fisher's protected least significant difference. This test gives no real protection against an excessive type I error rate, and should only be used for pre-planned orthogonal comparisons. Also generally too liberal is Duncan's multiple range test - statisticians have taken a special and not unjustified dislike to this test and using it will almost certainly draw negative comments from a reviewer.

Multiple comparison tests make the same assumptions as the original analysis of variance - and in fact are somewhat less robust to those assumptions being flouted. Chapter Contents. Nitrogen Content of Red Clover Plants. Waller-Duncan K-ratio t Test for Nitrogen. This test minimizes the Bayes risk under additive loss and certain other assumptions. Error Degrees of Freedom. Error Mean Square. F Value. Critical Value of t. Minimum Significant Difference. Means with the same letter are not significantly different.

Waller Grouping. Duncan's Multiple Range Test for Nitrogen.