(Axiomatic) Definition of probability
During the XXth century, a Russian mathematician, Andrei Kolmogorov, proposed a definition of probability, which is the one that we keep on using nowadays.
If we do a certain experiment, which has a sample space
-
The probability of any event
is positive or zero. Namely . The probability measures, in a certain way, the difficulty of event happening: the smaller the probability, the more difficult it is to happen. -
The probability of the sure event is
. Namely . And so, the probability is always greater than and smaller than : probability zero means that there is no possibility for it to happen (it is an impossible event), and probability means that it will always happen (it is a sure event). - The probability of the union of any set of two by two incompatible events is the sum of the probabilities of the events. That is, if we have, for example, events
, and these are two by two incompatible, then
Note: In mathematics, an axiom is a result that is accepted without the need for proof. In this case, we say that this is the axiomatic definition of probability because we define probability as a function that satisfies these three axioms. Also, we might have chosen different axioms, and then probability would be another thing.
Main properties of probability
.
That is, the probabilities of complementary events add up to
Let's see why. We know that, on the one hand,
This property, which turns out to be very useful, can be generalized:
If we have three or more events, two by two incompatible, and such that their union is the whole sample space, that is to say,
We say in this case that
As a result
- If
, then .
The notation "if
This property is quite logical: if, after throwing a dice, we want to compare the probability of
.
This result, which is very important to remember, is a consequence of something that you can see in the sets cell: given two sets, A and B, you can express its union as
In the Sets Teory we have that
Similarly,
Replacing these probabilities in the equality, we find
Now, we can solve some problems.
Example
A dice of six faces is tailored so that the probability of getting every face is proportional to the number depicted on it.
1 What is the probability of extracting a
In this case, we say that the probability of each face turning up is not the same, therefore we cannot simply apply the rule of Laplace. If we follow the statement, it says that the probability of each face turning up is proportional to the number of the face itself, and this means that, if we say that the probability of face
Now, since
Therefore
And so, the probability of extracting
2 What is the probability of extracting an odd number?
The cases favourable to event
Example
Tomorrow there is an exam. Esther has studied really hard, and she only has
David has studied less, and he has
What is the probability that at least one of them does not pass the exam?
The first thing that we must do is express the problem as we know how, i.e., with events. We define the events
From the statement, we know that
We might think that if Esther has probability
If we compute it this way, we are assuming that the events
Therefore, the correct way of calculating this probability is using the formula that we have seen before:
by replacing with the results that we know, we get
or what amounts to the same,