You've got to state the problem little bit more clear.
What do you mean by "set"? Is it a list of certain possible values,
available as outcomes of each single measurement (variate)? Or is it
something else?
How many variates do you have inside each sample?
What is it exactly that you want to find?
Do you want just to compare sample #1 and #2? There seems to be not enough
variates for reliable result. Still, you may want to look at central
tendencies (mean, median), i.e. location shift of samples, homogeneity of
their variances, or the overall shape of empirical distributions. If your
data are NOT normally distributed, you may use Wilcoxon rank sum test for
medians,Kolmogorov-Smirnov for comparing empirical distribution functions
and median-centering Fligner-Killeen test for homogeneity of variances.
Or may be you are in fact looking for something else? May be you suspect
that variates inside each sample vary together, according to some outside
force? In that case you may want to calculate correlation coefficient -
Perason product-moment for normal and Spearman for NOT normal data.
All in all it seems like you need to consult some statistical textbook = )
Socal and Rolf is a good choice
setrofim wrote:>
> I have a bunch of benchmark measurements that look something like this:
> sample.1 0.0000066660 0.0000062500 0.0000058330 0.0000058330
> 0.0000058330
> ...
> i.e each measurement take on one of a set of values. The set values
isn't
> fixed, but they seem to go up increments; in this case, it appears to be
> about 4.17e-07 (e.g. it would be impossible for a measurement to be
> 0.0000066440).
> What is way to test for significant differences between two samples?
>
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