I am projecting forward two fish stock populations for a 10 year period with different levels of fishing mortality and am trying to compare mean population sizes at the end of each year to see if there is a statistical difference if fishing pressure is reduced.

The t-test results are confusing me though. I'm using a fisheries model to project the population forward and I can choose the number(n) of iterations/runs per year. So what I am left with to analyze are a number of arrays of n possible population sizes (as in the example below where n=4). I then conduct a t-test between the mean population size in 2008 for population A and population B and do the same for 2009. This leaves me with 2 t-scores, one for 2008 and one for 2009.

I have conducted a t-test with n=100, n=1000 and n=10,000. I would have expected the t-scores not to change much between each of these and was basically increasing n just to increase my confidence in the outcome. However, I found that at n=100 only about 3% of the time are the results significant. At n=1000 this rises to 35% and at n=10,000 everything is significant.

I have spent days trying to figure out why increasing n has this effect on the t-score but have not been able to get anywhere. If anybody can shed some light on this I'd be very grateful.

thanks in advance.

Barry

Population A Population B

2008 2009 2008 2009

27806 33694 35210 40738

41832 39478 21677 21851

16868 15653 35363 33170

45645 44100 25917 33957