Two-way anova with interaction term: what is the point of a post-hoc test? I have a statistics interpretation question.  I've recently performed a two-way anova to identify an interaction term between my categorical independent variables (genotype + temperature) that influences my continuous dependent variable (speed). My hypothesis is that genotype+temperature strongly interact to reduce speed.
In my case, the interaction term is significant - ie., genotype and temperature significantly interact, and I can easily observe that speed is reduced in this case.
I am now directed by common practice to perform a post-hoc test, but I don't really understand why:
What post-hoc test is appropriate to help confirm the interaction between temp and genotype?  I am not interested in anything except the interaction, as I consider all of the other conditions to be controls.
That said, is such a post-hoc test necessary, given that the ANOVA itself is designed to (and succeeds) in revealing a significant interaction?  Is it necessary to determine the sign of the interaction?
Bonus points if you can point me to a nonparametric test for this, as my data is not gaussian (but my large N=100+ helps me be comfortable with using the data in the ANOVA itself).
 A: A relatively unknown but very useful nonparametric substitute for two-way ANOVA with replication (must be balanced ANOVA) is the Schierer-Ray-Hare test. It is an extension of the Kruskal-Wallis test. Do it this way:


*

*Replace each data observation with its overall rank (lowest number is ranked 1 and tied observations are all given the average rank)

*Run the two-way ANOVA as usual with the ranks instead of the actual data values.

*Discard the MS, F, and p value terms in the ANOVA output.

*Sum SS for SS factors, SS interaction, and SS error. Divide this sum by df total. The result is MS total.

*The test statistic, H, for each factor and interaction equals its SS / MS total

*The Excel formula for the p value for each is:  CHIDIST(H, df). The df is the usual df for each factor and interaction. The Excel output provides these df figures.


The Schierer-Ray-Hare test is a lot less powerful that regular two-way ANOVA. The p values are usually around twice as large on the SRH test as those generated by two-factor ANOVA.
