"In probability, we define an event as a subset of the sample space. In other words, an event is a group of related outcomes (though an event might contain just one outcome, or even zero). I always thought this was a funny definition for the word “event”: it’s not the first thing that word brings to mind. But it turns out to be a useful concept, because sometimes we’re not interested in any particular outcome necessarily, but rather in whether the outcome — whatever it is — has a certain property. For instance, suppose at the start of some game, my opponent and I each roll the die, agreeing that the highest roller gets to go first. Suppose he rolls a 2. Now it’s my turn. The Ω for my die roll is of course { 1, 2, 3, 4, 5, 6 }. But in this case, it doesn’t necessarily matter what my specific outcome is; only whether I beat a 2. So I could define the event M (for “me first”) to be the set { 3, 4, 5, 6 }. I could define the event H (“him first”) to be the set { 1 } (notice H is still a set, even though it has only one element.) Then I could define the event T (“tie”) as the set { 2 }. I’ve now effectively collapsed a larger set of outcomes into only the groups of outcomes I’m interested in. Now I’m all ready to reason about the likelihood that each of these events actually occurs."