The paper studies sequential information acquisition under ambiguity about the relevant states in a setting where an agent decides for how long to collect information, and what kind of information to collect, before taking irreversible action. The agent optimizes against the worst-case belief and updates prior by prior. We show that the consideration of ambiguity gives rise to rich dynamics: compared to the Bayesian DM, the DM here tends to experiment excessively when facing modest uncertainty and, to counteract it, may stop experimenting when facing high uncertainty. In the latter case, the DM's stopping rule is non-monotonic in beliefs and features randomized stopping. The DM may also split attention to multiple news sources even when her Bayesian counterpart would not.