Inventory management can be challenging due to uncertainty about the underlying demand. When attempting to construct probabilistic models of demand based on past data, demand samples are almost never available: only sales data can be used. The latter limitation, referred to as demand censoring, introduces a trade-off between instantaneous performance and information collection. Much of the literature has sought to understand how operational decisions should be modified to incorporate this trade-off. In this talk, we ask an even more basic question:　When does this trade-off matter in the first place?  Specifically, we focus on two questions: i.) what is the value of accounting for the exploration-exploitation trade-off; and ii.) what is the cost imposed by having access only to sales data as opposed to underlying demand samples?　We analyse these questions in the context of a well-studied stationary multi-period newsvendor problem, in which a retailer sells perishable items and unmet demand is lost and unobserved. The demand distribution is initially known only up to a parameter, for which the decision maker has a prior. Quite remarkably, we show that, for a broad family of tractable cases, there is essentially no exploration-exploitation trade-off, i.e., there is almost no value of accounting for the impact of ordering decisions on information collection. This insight holds for all service levels and time horizons. Moreover, we 　establish that losses due to demand censoring (as compared to having full access to demand samples) are limited, but these are of higher order than those due to ignoring the exploration-exploitation trade-off. In other words, effort to improve information collection about lost sales is more valuable than analytic or computational effort to pin down the optimal policy in the presence of censoring.