Moments of a distribution are defined as $E(X), E(X^2), E(X^3) \dots$ These may also be referred to as population moments, and each population moment has a corollary sample moment (provided you have data from a sample). The first population or sample moment is the mean by definition. The second moment, $E(X^2)$ is the probability weighted average of the squares of the values in the population. $\frac1n \sum_{i=1}^{n} X_i^2$ The kth population moment is $\mu_k = E(X^k)$