Which test is more suitable for plant science? I have plants which are treated with two different chemicals, so I have control plants, and two variants with above mentioned chemicals. Those plants are grown in three different types of medium. I need to compare all variants with each other.
I tried both tests and I got different results. Is there any rule for which post hoc test I should choose here?
You may find these of help
http://www2.hawaii.edu/~taylor/z631/multcomp.pdf
Overview of methods
There are many methods for multiple-comparisons. Most are for pairwise comparisons of
group means, to determine which are significantly different from which others. Other
methods, though, are for more specialized purposes (e.g. comparing each of several treatments
to a control) or allow testing of more-general hypotheses contrasting sets of group
means (as is often done in preplanned “contrasts”).
The various methods differ in how well they properly control the overall significance level
and in their relative power; some, such as the popular “Duncan’s multiple range test” do not
control the overall significance level. The ones described in this handout all adequately
control overall significance and are either easy to use or powerful. They are:
• Bonferroni extremely general and simple, but often not powerful
• Tukey’s the best for all-possible pairwise comparisons when sample sizes are
unequal or confidence intervals are needed; very good even with equal
samples sizes without confidence intervals
• stepdown the most powerful for all possible pairwise comparisons when sample sizes
are equal
• Dunnett’s for comparing one sample (“control”) to each of the others, but not comparing
the others to each other.
Multiple-Comparison Procedures 2 of 9
• MCB compares each mean to the “best” (largest or smallest, as you specify) of
the other means.
• Scheffé’s for unplanned contrasts among sets of means
When more than one method is applicable, and it is not clear which is more efficient (i.e. will
give the narrower CI, for given α), it is legitimate to try all the applicable methods and use the
one which proves most efficient for the given data.
You may find these of help
http://www2.hawaii.edu/~taylor/z631/multcomp.pdf
Overview of methods
There are many methods for multiple-comparisons. Most are for pairwise comparisons of
group means, to determine which are significantly different from which others. Other
methods, though, are for more specialized purposes (e.g. comparing each of several treatments
to a control) or allow testing of more-general hypotheses contrasting sets of group
means (as is often done in preplanned “contrasts”).
The various methods differ in how well they properly control the overall significance level
and in their relative power; some, such as the popular “Duncan’s multiple range test” do not
control the overall significance level. The ones described in this handout all adequately
control overall significance and are either easy to use or powerful. They are:
• Bonferroni extremely general and simple, but often not powerful
• Tukey’s the best for all-possible pairwise comparisons when sample sizes are
unequal or confidence intervals are needed; very good even with equal
samples sizes without confidence intervals
• stepdown the most powerful for all possible pairwise comparisons when sample sizes
are equal
• Dunnett’s for comparing one sample (“control”) to each of the others, but not comparing
the others to each other.
Multiple-Comparison Procedures 2 of 9
• MCB compares each mean to the “best” (largest or smallest, as you specify) of
the other means.
• Scheffé’s for unplanned contrasts among sets of means
When more than one method is applicable, and it is not clear which is more efficient (i.e. will
give the narrower CI, for given α), it is legitimate to try all the applicable methods and use the
one which proves most efficient for the given data.
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