We give algorithms for estimating the expectation of a given real-valued function $\phi:X\to {\bf R}$ on a sample drawn randomly from some unknown distribution $D$ over domain $X$, namely ${\bf E}_{{\bf x}\sim D}[\phi({\bf x})]$. Our algorithms work in two well-studied models of restricted access to data samples... The first one is the statistical query (SQ) model in which an algorithm has access to an SQ oracle for the input distribution $D$ over $X$ instead of i.i.d. samples from $D$. Given a query function $\phi:X \to [0,1]$, the oracle returns an estimate of ${\bf E}_{{\bf x}\sim D}[\phi({\bf x})]$ within some tolerance $\tau$. The second, is a model in which only a single bit is communicated from each sample. In both of these models the error obtained using a naive implementation would scale polynomially with the range of the random variable $\phi({\bf x})$ (which might even be infinite). In contrast, without restrictions on access to data the expected error scales with the standard deviation of $\phi({\bf x})$. Here we give a simple algorithm whose error scales linearly in standard deviation of $\phi({\bf x})$ and logarithmically with an upper bound on the second moment of $\phi({\bf x})$. As corollaries, we obtain algorithms for high dimensional mean estimation and stochastic convex optimization in these models that work in more general settings than previously known solutions. (read more)