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However, the studentized range distribution used to determine the level of significance of the differences considered in Tukey's test has vastly broader application: It is useful for researchers who have searched their collected data for remarkable differences between groups, but then cannot validly determine how significant their discovered ...
Compact Letter Display (CLD) is a statistical method to clarify the output of multiple hypothesis testing when using the ANOVA and Tukey's range tests. CLD can also be applied following the Duncan's new multiple range test (which is similar to Tukey's range test).
The most common setting for Tukey's test of additivity is a two-way factorial analysis of variance (ANOVA) with one observation per cell. The response variable Y ij is observed in a table of cells with the rows indexed by i = 1,..., m and the columns indexed by j = 1,..., n. The rows and columns typically correspond to various types and levels ...
It is advised to check the references for photos of reaction results. [1] Reagent testers might show the colour of the desired substance while not showing a different colour for a more dangerous additive. [2] For this reason it is essential to use multiple different tests to show all adulterants.
Tukey's test is either: Tukey's range test, also called Tukey method, Tukey's honest significance test, Tukey's HSD (Honestly Significant Difference) test;
Tukey’s Test (see also: Studentized Range Distribution) However, with the exception of Scheffès Method, these tests should be specified "a priori" despite being called "post-hoc" in conventional usage. For example, a difference between means could be significant with the Holm-Bonferroni method but not with the Turkey Test and vice versa.
It is identical to a Tukey mean-difference plot, [1] the name by which it is known in other fields, but was popularised in medical statistics by J. Martin Bland and Douglas G. Altman. [ 2 ] [ 3 ] Construction
[5] [6] Unlike Tukey's range test, the Newman–Keuls method uses different critical values for different pairs of mean comparisons. Thus, the procedure is more likely to reveal significant differences between group means and to commit type I errors by incorrectly rejecting a null hypothesis when it is true.