Epsilon-sufficient set ($\mathrm{Pr}(X\in S)\ge 1-ϵ$)

For a finite-valued random variable, an epsilon-sufficient set $S$ retains probability at least $1-ϵ$. This notion is about covering probability mass and is unrelated to a sufficient statistic. For an IID random variable source, any such set of words with fixed $ϵ<1$ requires exponentially many words at a rate approaching the Shannon entropy.

For finite-alphabet independent and identically distributed random variables, fixed $0\le ϵ<1$ and $\delta >0$, every epsilon-sufficient set $S_n$ of length-$n$ words satisfies $|S_n|\ge (1-ϵ)2^{n(H-\delta )}/2$ for sufficiently large $n$. Intersect $S_n$ with a typical set whose excluded mass is at most $(1-ϵ)/2$. The intersection retains that much probability and each word has probability at most $2^{-n(H-\delta )}$.