Hashing
> The Avalance Effect<p>> Another way hash functions get evaluated is on something called the "avalanche effect." This refers to how many bits in the output value change when just a single bit of the input changes. To say that a hash function has a good avalanche effect, a single bit flip in the input should result in an average of 50% the output bits flipping.<p>I think it's important to note that this isn't a property that is necessary for a hash function, there are hash functions that deliberately try to minimize the avalanche effect, such as in locality sensitive hashing; which is another form of hash function that has different use cases that are also very cool.<p>You can use LSHs to for example remove near duplicate search results in a search engine without having to actually comparing the texts, even tolerating single-word differences; or to help with nearest neighbor searches.<p>A lot of what's written about hash function tends to assume you want to use them for whatever thing the author has in mind, it's not a unique fault of the author -- many textbooks have this problem, and add all these properties to them that are accidental to what a hash function actually is; it's really just a mapping function to a fixed width representation with an even distribution across the domain.