We consider worst-case identification in H-infinity, in a framework that includes both time-domain and frequency-domain experiments. We show that any linear algorithm-including least squares-for identification of n-parameter finite impulse response (FIR) models in the presence of disturbances of size epsilon has worst-case error growing at least as fast as epsilon log n. This error bound is optimal, both for time-domain and frequency-domain identification.