# 3.1: A bit of information theory


A bit is a binary digit; it is also a unit of information. If you have one bit, you can specify one of two possibilities, usually written 0 and 1. If you have two bits, there are 4 possible combinations, 00, 01, 10, and 11. In general, if you have $$b$$ bits, you can indicate one of $$2^{b}$$ values. A byte is 8 bits, so it can hold one of 256 values.

Going in the other direction, suppose you want to store a letter of the alphabet. There are 26 letters, so how many bits do you need? With 4 bits, you can specify one of 16 values, so that’s not enough. With 5 bits, you can specify up to 32 values, so that’s enough for all the letters, with a few values left over.

In general, if you want to specify one of $$N$$ values, you should choose the smallest value of $$b$$ so that $$2^{b} \leq N$$. Taking the $$\log$$ base 2 of both sides yields $$b \leq \log_{2}{N}$$.

Suppose I flip a coin and tell you the outcome. I have given you one bit of information. If I roll a six-sided die and tell you the outcome, I have given you $$\log_{2}{6}$$ bits of information. And in general, if the probability of the outcome is 1 in $$N$$, then the outcome contains $$\log_{2}{N}$$ bits of information.

Equivalently, if the probability of the outcome is $$p$$, then the information content is $$-\log_{2}{p}$$. This quantity is called the self-information of the outcome. It measures how surprising the outcome is, which is why it is also called surprisal. If your horse has only one chance in 16 of winning, and he wins, you get 4 bits of information (along with the payout). But if the favorite wins 75% of the time, the news of the win contains only 0.42 bits.

Intuitively, unexpected news carries a lot of information; conversely, if there is something you were already confident of, confirming it contributes only a small amount of information.

For several topics in this book, we will need to be comfortable converting back and forth between the number of bits, $$b$$, and the number of values they can encode, $$N = 2^{b}$$.

This page titled 3.1: A bit of information theory is shared under a CC BY-NC license and was authored, remixed, and/or curated by Allen B. Downey (Green Tea Press) .