What is a hash function's avalanche effect and collision resistance?

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The main idea here is how a hash function behaves in two key ways: diffusion of input changes (the avalanche effect) and the difficulty of finding collisions (collision resistance). The avalanche effect means that flipping a single bit (or making a tiny change) in the input should produce a dramatically different hash output, so small differences don’t produce similar hashes. This makes the hash output look random and uniform, which helps prevent attackers from inferring anything about the input from the hash value. Collision resistance means it should be computationally hard to find two different inputs that produce the same hash. If that were easy, someone could substitute a different piece of data with the same hash, undermining integrity.

So, the best description says a small input change yields a large output change, and it’s hard to find two inputs that produce the same hash. The other statements don’t fit: one claims the hash value simply increases with input size, which isn’t how hash outputs are intended to behave; another says it’s easy to find two inputs with the same hash, which contradicts collision resistance; and a statement that the avalanche effect applies only to cryptographic encryption misframes what the effect is and how it relates to hash functions.