Recall that an $\epsilon$-biased space is a set $S \subset \{0,1\}^n$ such that for every non-zero linear test $\alpha \in \{0,1\}^n \setminus \{0\}^n$, the expected bias $$| \mathbb{E}_{x \in S} [ (-1)^{<x,\alpha>} ] | \leq \epsilon$$ is small. Distributions over such subsets $\epsilon$-fool all linear tests, so if we can make $|S|$ as small as possible, we have a good deranomization primitive.
Is an average case analogue of such a construction studied anywhere, and is it useful? More precisely, suppose we take a subset $S \subset \{0,1\}^n$ such that $$\mathbb{E}_{\alpha}| \mathbb{E}_{x \in S} [ (-1)^{<x,\alpha>} ] | \leq \epsilon$$ This is, of course, weaker than $\epsilon$-fooling all linear tests. My question is: is there a natural approach to constructing such a set $S$ of small size?
A method used to construct $\epsilon$-bias sets is to interpret it as the vectors of the parity-check matrix of an $\epsilon$-balanced linear code, and so the aim becomes to design such a code with as high rate as possible. Would such an approach work for the average-case too? I am unclear on the correspondence though.