The information content (also called the surprisal) of an event E {\displaystyle E} is an increasing function of the reciprocal of the probability p ( E ) {\displaystyle p(E)} of the event, precisely I ( E ) = − log 2 ⁡ ( p ( E ) ) = log 2 ⁡ ( 1 / p ( E ) ) {\displaystyle I(E)=-\log _{2}(p(E))=\log _{2}(1/p(E))} . Entropy measures the expected (i.e., average) amount of information conveyed by identifying the outcome of a random trial. This implies that casting a die has higher entropy than tossing a coin because each outcome of a die toss has smaller probability (about p = 1 / 6 {\displaystyle p=1/6} ) than each outcome of a coin toss ( p = 1 / 2 {\displaystyle p=1/2} ).