Question 1

What is Shannon entropy?

Accepted Answer

Shannon entropy is a measure of the average information content or unpredictability in a message. Measured in bits per character, a higher entropy value means the text is more random or diverse. For example, a string of all identical characters has 0 bits of entropy, while perfectly random data approaches the theoretical maximum.

Question 2

What is the maximum possible entropy?

Accepted Answer

The maximum entropy for a given text equals log₂(N) bits, where N is the number of unique characters in the input. This maximum is achieved only when every unique character appears with exactly equal frequency. The relative entropy percentage shows how close the actual entropy is to this theoretical maximum.

Question 3

How is the character frequency table useful?

Accepted Answer

The frequency table shows how often each character appears and its percentage share of the total input. Characters are sorted from most to least frequent. This is useful for cryptanalysis, data compression analysis, language detection, and understanding the distribution of symbols in any text or binary data.

Question 4

What can entropy values tell me about my data?

Accepted Answer

Low entropy (close to 0 bits) means your data is highly repetitive or structured. High entropy (close to the maximum) means your data is diverse and random. Encrypted or compressed data typically has entropy near its maximum. Natural language text usually has entropy between 4 and 5 bits per character in English.

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