General definitions of probability#

For introduction, let’s compare how statistical and classical probabilites relate to each other.

Source: Huippu, Otava

Statistical definition of probability#

Probabilities can be calculated based on experimental observations. The simplest example of such a probability is based on empirical or experimental data, known as statistical probability. In this case, the probability of an event is the same as the relative frequency of the corresponding statistical variable.

Definition

Statistical definition of probability is denoted by \(P(A)=\frac{f_A}{n}\) where \(f_A\) is the frequency of the event and \(n\) is the number of observations.

Classical definition of probability#

The classical definition of probability is based on equally likely outcomes in a universal set. According to this definition:

\(P(A) = \frac{\text{number of favourable outcomes for event A}}{\text{total number of possible outcomes}}\)

This definition assumes that all outcomes in the sample set are equally likely. It is commonly used in situations where each elementary outcome is equally likely to occur, such as in the tossing of a fair coin or a six-sided dice.

Example. Two six-sided dices are thrown. What are the odds that the dices have the same face?

Let’s denote \(A\) = ‘dices have the same face’ = ‘faces are ones OR faces are twos OR … OR faces are six’s’.

The favourable events are \(\{1,1\}\), \(\{2,2\}\), \(\{3,3\}\), \(\{4,4\}\), \(\{5,5\}\) and \(\{6,6\}\) so the number of favourable events is 6. When applying the multiplication principle, the total number of possible outcomes is \(6 \cdot 6=36\).

This can be visualized by sketching all the possible combinations.

Combinations of two dices

Fig. 19 All the combinations of two six-sided dices, where green cells represent the favourable events.#

Therefore, \(P(A) = \frac{6}{36} = \frac{1}{6}\)

Geometrical definition of probability#

For introduction…

Source: Huippu, Otava