The importance of vocabulary in reading comprehension emphasizes the need to accurately assess an individual's familiarity with words. The present article highlights problems with using occurrence counts in corpora as an index of word familiarity, especially when studying individuals varying in reading experience. We demonstrate via computational simulations and norming studies that corpus-based word frequencies systematically overestimate strengths of word representations, especially in the low-frequency range and in smaller-size vocabularies. Experience-driven differences in word familiarity prove to be faithfully captured by the subjective frequency ratings collected from responders at different experience levels. When matched on those levels, this lexical measure explains more variance than corpus-based frequencies in eye-movement and lexical decision latencies to English words, attested in populations with varied reading experience and skill. Furthermore, the use of subjective frequencies removes the widely reported (corpus) Frequency × Skill interaction, showing that more skilled readers are equally faster in processing any word than the less skilled readers, not disproportionally faster in processing lower frequency words. This finding challenges the view that the more skilled an individual is in generic mechanisms of word processing, the less reliant he or she will be on the actual lexical characteristics of that word.