What test to apply in comparative side-by-side experiment design?

I have the following experimental design: I have different (and disjoint) sets of data, and for every set of input data $s_1 ... s_n$ I generate the outputs $y_1 ... y_n$ and $z_1 ... z_n$ using two different algorithms $Y$ and $Z$.

Then I present each result pair $i$ in a side-by-side ($y_i \leftrightarrow z_i$, randomized) manner to $k$ human testers who provide me with a $score \in [-10;10]$ which side was better in their opinion:

At the end of the experiment I have $k \times n$ groups of scores in the range $[-10;10]$ per set.

What would be the best approach for significance testing (e.g. to test whether $Y$ is statistically different from $Z$ with given threshold $p$ and if not what would be the required sample size $k$)?

What would be the way to compare two different sets?

In the current design every tester provides the score for every pair. I want to limit the max amount of scores per tester per set to $m, m < n$, so that I only get $\frac{k \times m}{n}$ scores per pair. What tests could I apply in this case?