Measure the information content and unpredictability of a data source.
Probability/Counts Input
Values are normalized automatically.
Entropy Analysis
Information Entropy (H)1Bits Per Symbol
Source States2
Relative Entropy100.0%Max Uncertainty
Overview
Shannon Entropy quantifies the amount of 'surprise' or average information contained in each symbol produced by a source. It is measured in bits per symbol and represents the theoretical limit for lossless data compression.
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Pro Tips
Higher entropy values mean the source is more random and less predictable.
In cryptography, high entropy is essential for generating strong, unpredictable keys.
If you enter counts (like 20, 30), the calculator will convert them into probabilities for you.
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Fun Facts
"A fair coin toss has an entropy of exactly 1.0 bit, while a biased coin always landing heads has 0 bits of entropy because there is no uncertainty."
"The term was coined by Claude Shannon in his landmark 1948 paper, 'A Mathematical Theory of Communication', which founded the field of Information Theory."
"Entropy is maximized when all possible outcomes are equally likely, representing a state of maximum uncertainty."
