We have previously looked at finding probabilities for equally likely events
P_{(event)} = 

We need to look at a few things surrounding this.
Bias: Equally likely outcomes is built around the fact that events have an equal chance of happening, we call this fair (A fair coin, a fair dice).
The opposite of this is Biased. A biased dice for example could be weighted so that one number has a better chance of coming down on a certain number.
Random: for probability to work, the event has to be random. For example if you had a box with coloured balls in and picked one while looking at it, this isn't random. If you did it without looking, this would be random
Expectation: If I flipped a coin, the chance I would get a head is 1/2. But what does this actually mean? It means I would expect to get 1 out of every two as a head, so if I flipped a coin 100 times, I should get 1/2 x 100 = 50 heads.
In reality I might not, I may get 40 heads and 60 tails. We will discuss this further in the lesson Experimental probability.