I have three techniques, called A, B and C. Each can be used independently when trying to perform four related tasks (Tasks 1, 2, 3 and 4). I have run lots of tests, and tried all combinations of each technique being on or off. My results look something like this. Let's say that higher numbers are better.
$$ \begin{array}{l|r|r|r|r|r|r|r|r|r|} \mbox{Technique $A$} & - & - & - & - & X & X & X & X \\ \mbox{Technique $B$} & - & - & X & X & - & - & X & X \\ \mbox{Technique $C$} & - & X & - & X & - & X & - & X \\ \hline \mbox{Task $1$} & 433 & 277 & 911 & 492 & 686 & 4211 & 3775 & {\bf 9732}\\ \mbox{Task $2$} & 149 & 1063 & 5562 & {\bf 6035} & 3 & 58 & 1391 & 1708\\ \mbox{Task $3$} & 220 & 1278 & 7014 & {\bf 7018} & 10 & 97 & 2083 & 4452\\ \mbox{Task $4$} & 218 & 1255 & 6142 & {\bf 8656} & 1 & 73 & 1087 & 2056\\ \end{array} $$
Looking at the numbers, it seems that $B+C$ is good for Tasks 2,3 and 4, and that $A$ on its own is best for Task 1. But I want to say a bit more. I'd like to be quantitative if I can. My question is: can I deduce anything quantitative from this data? Or do I really need some measure of the variance of the observations? That is, I suspect the numbers might be different if I ran all the tests again.
:-). By the way, in the interests of full disclosure, I should probably mention that I followed Sergio's advice and have cross-posted on Cross-Validated. (http://stats.stackexchange.com/questions/109803/what-do-i-need-in-order-to-draw-conclusions-from-this-data) – John Wickerson Jul 29 '14 at 16:36