We propose a novel technique for analyzing adaptive sampling called the {\\em\nSimulator}. Our approach differs from the existing methods by considering not\nhow much information could be gathered by any fixed sampling strategy, but how\ndifficult it is to distinguish a good sampling strategy from a bad one given\nthe limited amount of data collected up to any given time. This change of\nperspective allows us to match the strength of both Fano and change-of-measure\ntechniques, without succumbing to the limitations of either method. For\nconcreteness, we apply our techniques to a structured multi-arm bandit problem\nin the fixed-confidence pure exploration setting, where we show that the\nconstraints on the means imply a substantial gap between the\nmoderate-confidence sample complexity, and the asymptotic sample complexity as\n$\\delta \\to 0$ found in the literature. We also prove the first instance-based\nlower bounds for the top-k problem which incorporate the appropriate\nlog-factors. Moreover, our lower bounds zero-in on the number of times each\n\\emph{individual} arm needs to be pulled, uncovering new phenomena which are\ndrowned out in the aggregate sample complexity. Our new analysis inspires a\nsimple and near-optimal algorithm for the best-arm and top-k identification,\nthe first {\\em practical} algorithm of its kind for the latter problem which\nremoves extraneous log factors, and outperforms the state-of-the-art in\nexperiments.\n