I conducted an experiment, testing three conditions with 27 participants in a repeated measures design. Since the data is not normally distributed I used a Friedman test that reported a p-value of 0.1, a slight disappointment given how much time I spent in the lab. Being disappointed but curious, I still performed Wilcoxon's signed rank test as post-hoc analyses for the three pairs. The interesting thing is that these tests reported p-values 0.002, 0.004 and 0.25, respectively. Even after a - conservative - Bonferroni correction two of the p's are still significant.
Now, besides the ethical/scientific issues surrounding digging around in the data like that: why are the p-values of the Friedman and the Wilcoxon tests so far apart? And: how do I interpret these findings? They seem to contradict each other.