  We give a distribution-free testing algorithm for decision lists with
$\tilde{O}(n^{11/12}/\varepsilon^3)$ queries. This is the first sublinear
algorithm for this problem, which shows that, unlike halfspaces, testing is
strictly easier than learning for decision lists. Complementing the algorithm,
we show that any distribution-free tester for decision lists must make
$\tilde{\Omega}(\sqrt{n})$ queries, or draw $\tilde{\Omega}(n)$ samples when
the algorithm is sample-based.
