Random Variables and Probability


Die are easy to think about, because we’ve all rolled a die before and we all think we know what we mean when we say the probability of rolling a $1$ is $1/6$. Throughout this section, don’t let this intuition go. Rather expand upon it to the more detailed descriptions below.

Random Variable

A random variable is a function from an event to a numerical value. Note that the randomness is not, per se, part of the random variable. The randomness is instead found in the underlying process that the random variable is meant to quantify.

A die is especially easy to think about, but for clarity let’s imagine a die labeled with the letters $A, B, C, D, E, F$ instead of the numbers $1, 2, 3, 4, 5, 6$. As far the events go, rolling an $A$ will still happen with probability $1/6$. This special die helps us separate the distinct pieces of random variables. With this special die, we have events – any value of interest that a die might turn up – and values produced by the random variable associated with those events. In notation, we might write $X(A) = 1$, $X(B) = 2, \ldots, X(F) = 6$.

In mathematical statistics, we read $X \sim Uniform(\{A, B, C, D, E, F\})$, the random variable $X$ follows a discrete Uniform distribution on the set $\{A, B, C, D, E, F\}$. If you’re content to keep numbers on your die to enable cleaner notation we read $X \sim \text{Uniform}(1, 6)$, the random variable $X$ follows a discrete Uniform distribution on the set $\{1, 2, 3, 4, 5, 6\}$. Notice that the notation $\text{Uniform}(1, 6)$ implies the integer values in between $1$ and $6$. The latter notation is more common.

In fact, it’s common to drop the argument to the random variable, which is really a function, entirely. More often interest lies in the probability of events. For example, we might be interested in the probability that either $A, B,$ or $C$ turn up. Let $E = \{1, 2, 3\}$ be the event that an $A, B$, or $C$ turns up in one roll. We read $P(X \in E)$ what is the probability that we roll one of $A, B,$ or $C$. At a certain point, the argument to $X$ just gets in the way since the notation $P(X \in E)$ equally applies to a die labeled with letter or numbers.

Consider another random variable, also named $X$. Let $X \sim U(0, 1)$ be a discrete Uniform random variable on the numbers $0$ and $1$. Note that this could reasonably represent a fair coin, since we are willing to drop the events ${H, T}$ from our notation. Next, we will consider what the following mathematical statements means, in an operational sense, $P(X \in \{ H \}) = 1/2$.


Retired professor M. K. Smith provides a nice survey of the various notions of the probability of an event. This book will focus on an empirical version of probability that goes as follows.

The probability of an event $E$ is the limiting relative frequency of the occurrences of $E$ over the number of trials,

where $1(X \in E)$ takes on the value $1$ any time the random variable $X$ is in the event $E$ and $0$ otherwise. We interpret probability as if the process that produces $X$ were repeated (thus assuming repeatability) an infinite number of times. In terms of a fair coin, $P(X \in \{H\}) = 1/2$ implies that we believe that flipping a fair coin an infinite number of times would witness one half of the flips to produce head.