Several well-studied models of access to data samples, including statistical queries, local differential privacy and low-communication algorithms rely on queries that provide information about a function of a single sample. (For example, a statistical query (SQ) gives an estimate of Ex∼D[q(x)] for any choice of the query function q : X → R, where D is an unknown data distribution.) Yet some data analysis algorithms rely on properties of functions that depend on multiple samples. Such algorithms would be naturally implemented using k-wise queries each of which is specified by a function q : Xκ → R. Hence it is natural to ask whether algorithms using k-wise queries can solve learning problems more efficiently and by how much. Blum, Kalai, Wasserman  showed that for any weak PAC learning problem over a fixed distribution, the complexity of learning with k-wise SQs is smaller than the (unary) SQ complexity by a factor of at most 2κ. We show that for more general problems over distributions the picture is substantially richer. For every k, the complexity of distribution-independent PAC learning with κ-wise queries can be exponentially larger than learning with (k + 1)-wise queries. We then give two approaches for simulating a k-wise query using unary queries. The first approach exploits the structure of the problem that needs to be solved. It generalizes and strengthens (exponentially) the results of Blum et al. . It allows us to derive strong lower bounds for learning DNF formulas and stochastic constraint satisfaction problems that hold against algorithms using kwise queries. The second approach exploits the k-party communication complexity of the k-wise query function.