Please, I'm not very confident in statistics, and I'm trying to respond to a reviewer for a paper on the following issues:
In my experiment I observed 15 babies during a test where they were free to play with an experimental toy, for 10 minutes
- each baby was tested individually
- age ranges from 8 months to 36 months
- during the test I recorder the durations in seconds of 12 selected behaviours (i.e. how long the baby smile? how long the baby explore the toy? and so on...)
In order to see if there were differences due to the age, I split the group in two samples (threshold: 24 months) with N=7 and N=8.
I then run a Wilcoxon rank sum test to compare, for each behaviour, the averages of durations, obtaining 12 p values, some of which are significant (values lower than alpha=0.05 )
The reviewer says that I need to correct alpha with Bonferroni, as I'm performing a multiple testing.
This leads alpha to be very low:
alpha corrected = 0.05/12 = 0.004
with the consequence that all significant results disappear.
Now, googling a little bit, I found that Bonferroni is not a good method when comparisons are more that 3 or 4, as it is too conservative, and False Discovery Rate (FDR) is proposed instead.
Do you agree on this?