# What metric should I use to determine a significant effect?

I am not a statistician and hope someone can point me towards the right direction. I have some time series data grouped into three classes like this:

Time Period 1       Time Period 2     Time Period 3
-------------------------------------------------
[1,2,3,4,5,6...]    [12,13,14,15]     [17,3,1,3,4...]
[1,3,5,6,8,9...]    [6,8,7,9,6,4]     [1,2,5,7,3,2...]
[9,8,9,9,8,9...]    [3,1,1,2,1,2]     [7,8,9,9,9,8...]


The dots indicate that I have significantly more values for Time Period 1 and Time Period 3 than for Time Period 2. I am trying to define events of "interest". Interest is when there is a significant change in the transitions from Time Period 1 to Time Period 2 and Time Period 2 to Time Period 3. Of course, there could be a knob for determining what I mean for significant.

What I am looking for is a good metric that tells me if an event is of potential interest. Obviously, average would not do good due to outliers so all I could think of was the median which seemed like a good one i.e. if there is a significant change in the median from Time Period 1 to Time Period 2, then this transition is of interest.

While this metric is working out for me, I am curious if there is a more structured/formal approach to deriving a metric that is better than the median. As of now, the problem formulation is open as well so any suggestions/constructive criticisms are greatly appreciated.

-
The table shows multiple rows of data. Are the rows from different units of analysis (e.g., people)? And if yes, can they be assumed to be independent? – Wolfgang Jun 17 '11 at 13:33
@Wolfgang: Yes. These are different observation points and can be assumed to be independent (though this is just a conjecture at this point). Would you have some suggestions on how the analysis would change depending on the independence? I'm guessing that I might have to resort to some sort of multi-variate analysis if they are not independent. Please correct me if I am wrong. – Legend Jun 17 '11 at 18:09