  Contention resolution schemes (CRSs) are powerful tools for obtaining "ex
post feasible" solutions from candidates that are drawn from "ex ante feasible"
distributions. Online contention resolution schemes (OCRSs), the online
version, have found myriad applications in Bayesian and stochastic problems,
such as prophet inequalities and stochastic probing.
  When the ex ante distribution is unknown, it was unknown whether good
CRSs/OCRSs exist with no sample (in which case the scheme is oblivious) or few
samples from the distribution. In this work, we give a simple
$\frac{1}{e}$-selectable oblivious single item OCRS by mixing two simple
schemes evenly, and show, via a Ramsey theory argument, that it is optimal. On
the negative side, we show that no CRS or OCRS with $O(1)$ samples can be
$\Omega(1)$-balanced/selectable (i.e., preserve every active candidate with a
constant probability) for graphic or transversal matroids.
