Discrete Uniform Distribution


Since much of this class is about notation, this section attempts to build on your experience about dice in an effort to minimize the mental hurdles that follow the new notation. Expanding on the process of rolling a single die, we introduce a more formal definition of a random variable. Despite it’s name, random variables are 1) not random and 2) not variables. They get this name nonetheless because we think of them as variables that take on random values.

Using a fair die as an example of a random variable, we introduce a particular, but not the only, notion of probability. While it’s easy to get lost in the new notation and naturalness of this interpretation of probability, don’t lose sight of the world’s most interesting processes. Random variables are not always repeatable in an operational manner, and thus it’s not always obvious how probability is meant to be understood in tese contexts.

Warm Up

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.

As a warm up, let’s introduce a few new words based on the easy to think about dice example.

  • Experiment: An occurrence with an uncertain outcome that we can observe. For example, rolling a die.
  • Outcome: The result of an experiment; one particular state of the world. Sometimes called a “case.”
    For example: $4$.
  • Sample Space: The set of all possible outcomes for the experiment.
    For example, $\{1, 2, 3, 4, 5, 6\}$.
  • Event: A subset of possible outcomes that together have some property we are interested in.
    For example, the event “even die roll” is the set of outcomes $\{2, 4, 6\}$.
  • Probability: As Pierre-Simon Laplace said, the probability of an event with respect to a sample space is the number of favorable cases (outcomes from the sample space that are in the event) divided by the total number of cases in the sample space. (This assumes that all outcomes in the sample space are equally likely.) Since it is a ratio, probability will always be a number between 0 (representing an impossible event) and $1$ (representing a certain event).
    For example, the probability of an even die roll is $3/6 = 1/2$.

The specific definitions aboves come from Peter Norvig’s A Concrete Introduction to Probability (using Python), which is a great resource if you want more information about the basics of probability.

Random Variable

A random variable is a function from abritrary sets of the sample space to a numerical value. Despite the name, the randomness is not, per se, part of the 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, because it maps so well to a random variable. But for the sake of 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 $1/6$; there’s only one $A$ and $6$ possible outcomes, hence $1/6$. 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 mathematical 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 from $1$ to $6$, inclusive. The notation $X \sim \text{Uniform}(1, 6)$ 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 ${T, H}$ from our notation. Next, we will consider what the following mathematical statements means, in an operational sense, $P(X \in \{ 1 \}) = P(X = 1) = 1/2$.


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 experiments $N$,

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.

Probability in practice

Statistic attempts to approximate probabilities defined with respect to random variables. The most common approximation strategy, relative to the theoretical definition of probability above, is to simply drop the limit. Let’s define an approximation $\hat{P}(X \in E)$ to $P(X \in E)$ above.

In practice, we might let $X$ represent a coin. If we are interested in the event of flipping a heads, then we might flip this coin $N$ times and add up the total number of heads and divide by the total number of experiments. Take the time to notice that this is exactly what the $\hat{P}$ notation is saying mathematically.