Interpreting the results of Friedman test

Anyways, Friedman's test is a replacement for a two-way ANOVA and you
are comparing it to a one-way analysis, and the latter is likely just wrong.

Okay. Thanks for the hint.

Try

anova(lm(AUC~as.factor(Condition)+as.factor(Observer),data=dataForANOVA))

This results in p-value = 0.37969. This value is still quite different
from p-value = 1.913e-06, which is the result of friedman.test
(as.matrix(dataForFriedman)).

Is the method friedman.test (version R 2.9.0) working correctly for
certain types of data and hypotheses? Which limitations are known?

@ Jim: Thanks for the link.
Unfortunately, I'm a newbie in statistics, and I'm not sure, which
method can be used instead of the Friedman Test. Do you have an eye on
a certain R-program from this given website?

Doerte