In classical mechanics, entropy is a measure of the disorder in a system. Similarly, information is measured by the change in uncertainty in a system. Entropy, as defined by Shannon, is the uncertainty regarding which symbols are chosen from a set of symbols with given a priori probabilities. Since information is a decrease in uncertainty, we may regard entropy as the information required to construct the correct set of symbols. If there is more disorder, or entropy, then more information is required to reconstruct the correct set of symbols. Entropy and information are used interchangeably in Information Theory, although they do not always mean the same thing. Entropy in a source is equal to the information per symbol needed to reconstruct its output, and is given by H(p). However entropy in a channel decreases its information throughput. Entropy, as illustrated, can either support or obstruct our efforts to send information depending on where it occurs.