It looks like Analysis of Variance is your solution here. The assumptions made about your data must be as follows: - Observations are independent (has you data been collected properly) - Errors are normally distributed - Both groups have equal response variences You have given a very small sample; the following program tells me through a homogeneity of variences test that the differences in variences between the groups are within standard statistical parameters (only just). data temp; input low high; datalines; 0.1 1.1 0.5 0.4 0.4 1.4 0.6 0.6 0.1 0.2 0.5 0.2 0.6 0.8 ; data temp_1; team=1; set temp (keep=low rename=(low=score)); run; data temp_2; team=2; set temp(keep=high rename=(high=score)); run; data temp; set temp_1 temp_2; run; proc glm data=temp; class team; model score=team; means team / hovtest; quit; With such a small sample size it is very hard to say wether the errors are normally distributed. Having done our best to verify the assumptions (if you have more data you will be able to do this properly, just run a histrogram of the differenced between the mean and the observations for each group and verify that it looks like a bell curve), we can now proceed with an analysis of varience test, which is also included in the output of the GLM procedure of the abocve program. The differences in varience yeild an F-statistic (strength and consistency of difference between means) of 2.02. The chances of this happening randomly on the assumption that there was no difference between the groups is given to be around 18.1%, this is above the common statistical threshold of 5%, implying that we cannot conclude that the two groups are different based on our observations. Adding more observations to our data help us to determine with greater accuracy what is really the case here, as generally speaking when sample sizes are below 30 most statistical tests will be inconclusive. Hope this helps, -Murray
... View more
