Information Theory - Shannon's "Self-Information" units Shannon's "self-information" of the specific outcome "A" is given as: 
-log(Pr(A)), and the entropy is the expectation of the "self-information" of all the outcomes of the random variable. 
When the base of the log is 2, the units of information/entropy are called "bits". 
What is the best explanation the following simple question: 
Why do these information units are called "bits"?
 A: One good reason to call them bits is that this is the number of bits that you need on average to encode an outcome. Some Wikipedia articles you might want to take a look at are Huffman coding, arithmetic coding, entropy encoding and Shannon's source coding theorem.
To give a simple example, say outcome A has probability $1/2$ and outcomes B and C have probabilities $1/4$ each. Then you can encode A by $0$, B by $10$ and C by $11$. This is an optimal prefix-free code; the expected number of bits required to encode an outcome is $\frac12\cdot1+\frac14\cdot2+\frac14\cdot2=\frac32$, and since the number of bits in each code is the self-information (to base $2$) of the outcome it encodes, this expected number of bits is the entropy of the distribution.
A: Claude Shannon coined the term "bits" as a contraction of "binary digits".
If you have a source of random binary digits in which there is no bias or pattern of any sort, then the probability that the next binary digit to come out will be specifically a $1$ (or specifically a $0$, for that matter) is $1/2$, regardless of what any of the preceding digits show. Because the digits are independent, the probability that the next two digits will be something specific is $1/4$, and the probability that the next $n$ digits will be a specific seqence is $2^{-n}$.
Therefore, a sequence of random binary digits of arbitrary length is a good analogy for the surprise you find in anything else.
