Hi Andrew,

Thank you so much for your e-mail!

Followed the second question above, I know when we perform two-sample ttest on every connection of the network, we believe that the data is normally distributed for each stat; if we not sure the data is not normally distributed, we could do non parametric test, like two sample permutation test.

Now I am just thinking: if the data is normally distributed, I performed ttest on every connection of the network for the two groups; aftter FDR correction, I got some significant results. But now I also want to see if these  results are really "significant" rather than randomly significant,so what I want to do is:

step 1: I first use the real group data to do ttest for each single connection;let's say p < 0.05 for this test.

step 2: for the permutation part, I first disorganize the group and reorder the group, then perform ttest and get p-value;

step 3: iterate step 2 5000 times;

step 4: get the probability of p < 0.05 for the 5000 permutation, P = num(p<0.05)/5000; if P < 0.05, then I would say the result (for the real data) is significant.

I was wondering whether it is necessary to do "permutation test" like that or whether it is rational to do like that?

Again, thank you very much for your useful suggestions!

Have a nice one!
Ting