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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 $2^{-n(H-\delta )}$.

Fix $0\le ϵ<1$ and $\delta >0$. By the asymptotic equipartition property, all sufficiently large $n$ satisfy $\mathrm{Pr}(Z^{(n)}\notin T_{\delta }^n)\le (1-ϵ)/2$. For any epsilon-sufficient set $S_n$,Each word in this intersection has probability at most $2^{-n(H(Z)-\delta )}$. Hencewhich gives the minimum size of a sufficient set for an IID source:The Chebyshev inequality used above supplies the sufficient quantitative condition $n\ge 2v/[{\delta }^2(1-ϵ)]$. No assumption that $S_n$ itself is a typical set is needed; its intersection with one provides the bound.

The Shannon entropy of a finite-valued random variable isAn epsilon-sufficient set is a subset $S\subseteq {\mathcal{A}}_X$ retaining all but at most $ϵ$ of the probability:Usually $0\le ϵ\le 1$. Sufficiency here concerns retained probability mass, rather than a sufficient statistic.