Hello Frodo,

It is not clear to me from your questions some of the basics of your
analysis.

If you only have two levels of a factor, and one response - why in the anova
do you use more factors (and their interactions)?
In that sense, it is obvious that your results would differ from the t-test.

In either case, I am not sure if any of these methods are valid since your
data doesn't seem to be normal.
Here is an example code of how to get the same results from aov and t.test.
 And also a nonparametric option (that might be more fitting)


flat_550_W_realism =c(3,3,5,3,3,3,3,5,3,3,5,7,5,2,3)
flat_550_W_realism_AH =c(7,4,5,3,6,5,3,5,5,7,2,7,5, 5)

x <- c(rep(1, length(flat_550_W_realism)),
rep(2, length(flat_550_W_realism_AH)))

y <- c(flat_550_W_realism , flat_550_W_realism_AH)

# equal results between t test and anova
t.test(y ~ x, var.equal= T)
summary(aov(y ~ x))

# plotting the data:
boxplot(y ~ x) # group 1 is not at all symetrical...
wilcox.test(y ~ x) # a more fitting test



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On Wed, Jan 5, 2011 at 12:37 AM, Frodo Jedi <frodo.jedi@yahoo.com> wrote:

>
> I kindly ask you an help because I really don´t know how to solve this
> problem.
>

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