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The decision tree model computes a function by adaptively querying coordinates of an input. Each internal node specifies a query, its outgoing branches represent possible answers, and a leaf supplies the output. Later queries may depend on earlier answers. The number of queries on the longest root-to-leaf path is the decision-tree depth.^[ref]

For example, consider the Boolean function that returns one if any of its $n$ input bits is one. Querying in order can stop at the first one, but the all-zero input requires all $n$ queries: an unread bit might still change the output. Thus the worst-case query complexity is $n$.

If the input bits are independent and fair, that same procedure uses onlyqueries on average. Worst-case depth and average cost therefore answer different questions, and an average requires a specified input probability distribution.