What is probability?

3 minute read

We come across probability not just in statistics classrooms but also in real life. But, have you thought about what probability really means? I would like to introduce you to a formal definition of probability.

Consider a coin toss…

Let’s start with a simple example. Imagine tossing a fair coin where the outcome is either heads or tails. What is the probability of tossing heads? You’re right, \(0.5\). But why not \(-0.7\), \(20\), or \(\frac{1024}{7}\)? Well, the (somewhat boring) answer is because it is defined as such. To give you a more in depth answer, this post will introduce the formal way to define what a probability is.

The world of \(\Omega\), \(F\), \(P\).

Probability, or more formally, a probability space is defined using three letters: \(\Omega\), \(F\), \(P\). What are they? Let’s think a fair coin toss as an example.

\(\Omega\) is a sample space.

A sample space is a set of all the possible outcomes in a certain process. In a coin toss, a coin can only land heads (H) or tails (T), so there are the two only outcomes. So we have:

\[\Omega = \{ H,T \}\]

Each element in \(\Omega\) is an outcome and is often referred to as \(\omega\) (‘small’ omega).

\(F\) is a set of ‘events’.

First, let’s define an event. An event is a set that contains multiple outcomes. For example:

  • \(\{ H \}\)      is “an event that a coin lands heads”
  • \(\{ T \}\)      is “an event that a coin lands heads”
  • \(\{ H,T \}\) is “an event that a coin lands heads or tails”
  • \(\{ \}\)           is “an event that a coin lands neither heads nor tails”

In fact, these are the only events that are defined in a probability space of a coin toss! A set of events is literally a set that contains all the possible events in a certain process. Referring to the four events above, we have:

\[F = \{\{ \},\{ H \},\{ T \}\{ H,T \}\}\]

Note that \(F\) contains \(\Omega=\{ H,T \}\}\). This is always the case in any probability space. To more formally define \(F\), we need to introduce a more difficult concept called \(\sigma\)-algebra, but I’ll leave that to future posts for now.

\(P\) is a probability measure.

A probability measure is a function that takes in an event as input, and spits out a probability of that event between 0 and 1. Formally, \(P: F \rightarrow [0,1]\). In our example,

  • \(P(\{ H \})=0.5\)
  • \(P(\{ T \})=0.5\)
  • \(P(\{ H,T \})=1\)
  • \(P(\{ \})=0\)

For \(P\) to be a probability measure, we need two more conditions (axioms).

  • \(\begin{align}&P(\Omega)=1\end{align}\)
  • \(P(\bigcup_{j=1}^{\infty}A_j) = \sum_{j=1}^{\infty}P(A_j)\) where \(A_j\) are disjoint.

The first equation seems intuitive. Recall that \(\Omega\) contains all the outcomes that can possibly happen. This equation is saying that the probability of either one of the all possible outcomes happening is 1.

The second equation looks mysterious, so let me break it down. First, \(A_j\) being disjoint means that when one event is happening, another event cannot happen. For example \({H}\) and \({T}\) are disjoint, whereas \({H}\) and \({H,T}\) are not (because they overlap). Formally, two events \(A,B\) (or sets) are disjoint when \(A \cap B={}=\phi\). \(\phi\) is just a commonly used notation that refers to an empty set.

The equation is essentially saying that the probability of either one of the multiple disjoint events happening \(P(\bigcup_{j=1}^{\infty}A_j)\) is the same as the sum of the probability of each event happening (\(\sum_{j=1}^{\infty}P(A_j)\)).
For example, in our case, since \({H}\) and \({T}\) are disjoint and so it must be the case that


That’s it!

This is in fact, all we need to define what a probability is. As a side note, physicists did try defining a negative probability. I’m not going into any details about it, but you can read more about it if interested.

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