Final answer:
The statement is referring to Shannon's Theorem, which establishes a limit on lossless data compression based on the entropy of the source. It states that it's impossible to compress data below its Shannon entropy without losing information.
Step-by-step explanation:
Related to entropy, Shannon's Theorem states: it is impossible to compress the data such that the code rate is less than the Shannon entropy of the source, without it being virtually certain that information will be lost. The correct answer to the question is B) Shannon's Theorem.
Entropy, in the context of information theory, refers to the average amount of information produced by a stochastic source of data. When compressing data, Shannon's Theorem—or the Noiseless Coding Theorem—provides a fundamental limit on the efficiency of lossless data compression methods. It implies that, on average, a lossless compression scheme cannot compress sequences of symbols from a stochastic source below the average rate determined by the source's entropy, without losing some information. This principle underlies the functioning of various coding strategies in digital communication systems.