How much randomness can be extracted from memoryless Shannon entropy sources?

We revisit the classical problem: given a memoryless source having a certain amount of Shannon Entropy, how many random bits can be extracted? This question appears in works studying random number generators built from physical entropy sources. Some authors use a heuristic estimate obtained from the Asymptotic Equipartition Property, which yields roughly $n$ extractable bits, where $n$ is the total Shannon entropy amount. However the best known precise form gives only $n-O(\sqrt{\log(1/\epsilon) n})$, where $\epsilon$ is the distance of the extracted bits from uniform. In this paper we show a matching $ n-\Omega(\sqrt{\log(1/\epsilon) n})$ upper bound. Therefore, the loss of $\Theta(\sqrt{\log(1/\epsilon) n})$ bits is necessary. As we show, this theoretical bound is of practical relevance. Namely, applying the imprecise AEP heuristic to a mobile phone accelerometer one might overestimate extractable entropy even by $100\%$, no matter what the extractor is. Thus, the ``AEP extracting heuristic\u27\u27 should not be used without taking the precise error into account