We clarify the role of Kolmogorov complexity in the area of randomness

extraction. We show that a computable function is an almost

randomness extractor if and only if it is a Kolmogorov complexity

extractor, thus establishing a fundamental equivalence between two

forms of extraction studied in the literature: Kolmogorov extraction

and randomness extraction. We present a distribution ${\cal M}_k$

based on Kolmogorov complexity that is complete for randomness

extraction in the sense that a computable function is an almost

randomness extractor if and only if it extracts randomness from ${\cal

M}_k$.