Epistasis, or the context-dependence of the effects of mutations, limits our ability to predict the functional impact of combinations of mutations, and ultimately our ability to predict evolutionary trajectories. Information about the context-dependence of mutations can essentially be obtained in two ways: First, by experimental measurement the functional effects of combinations of mutations and calculating the epistatic contributions directly, and second, by statistical analysis of the frequencies and co-occurrences of protein residues in a multiple sequence alignment of protein homologs. In this manuscript, we derive the mathematical relationship between epistasis calculated on the basis of functional measurements, and the covariance calculated from a multiple sequence alignment. There is no one-to-one mapping between covariance and epistatic terms: covariance implies epistasis, but epistasis does not necessarily lead to covariance, indicating that covariance in itself is not the directly relevant quantity for functional prediction. Having calculated epistatic contributions from the alignment, we can directly obtain a functional prediction from the alignment statistics by applying a Walsh-Hadamard transform, fully analogous to the transformation that reconstructs functional data from measured epistatic contributions. This embedding into the Hadamard framework is directly relevant for solidifying our theoretical understanding of statistical methods that predict function and three-dimensional structure from natural alignments.