  Extreme value (EV) statistics of correlated systems are widely investigated
in many fields, spanning the spectrum from weather forecasting to earthquake
prediction. Does the unavoidable discrete sampling of a continuous correlated
stochastic process change its EV distribution? We explore this question for
correlated random variables modeled via Langevin dynamics for a particle in a
potential field. For potentials growing at infinity faster than linearly and
for long measurement times, we find that the EV distribution of the discretely
sampled process diverges from that of the full continuous dataset and converges
to that of independent and identically distributed random variables drawn from
the process's equilibrium measure. However, for processes with sublinear
potentials, the long-time limit is the EV statistics of the continuously
sampled data. We treat processes whose equilibrium measures belong to the three
EV attractors: Gumbel, Fr\'echet, and Weibull. Our work shows that the EV
statistics can be extremely sensitive to the sampling rate of the data.
